In this experiment, an efficient and accurate network of detecting automatically disseminated (bot) content on social platforms is devised. Through the utilisation of parallel convolutional neural network (CNN) which processes variable n-grams of text 15, 20, and 25 tokens in length encoded by Byte Pair Encoding (BPE), the complexities of linguistic content on social platforms are effectively captured and analysed. With validation on two sets of previously unexposed data, the model was able to achieve an accuracy of around 96.6% and 97.4% respectively — meeting or exceeding the performance of other comparable supervised ML solutions to this problem. Through testing, it is concluded that this method of text processing and analysis proves to be an effective way of classifying potentially artificially synthesized user data — aiding the security and integrity of social platforms.

In this paper we address the understanding of the problem, of why a deep learning model decides that an individual is eligible for a loan or not. Here we propose a novel approach for inferring, which attributes matter the most, for making a decision in each specific individual case. Specifically we leverage concepts from neural attention to devise a novel feature wise attention mechanism. As we show, using real world datasets, our approach offers unique insights into the importance of various features, by producing a decision explanation for each specific loan case. At the same time, we observe that our novel mechanism, generates decisions which are much closer to the decisions generated by human experts, compared to the existent competitors.

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

Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in market conditions prediction and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in whole-sale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine learning approach, the proposed method develops a financial optimum schedule with the incorporation of uncertainty information. Simulations performed with a concentrated solar power plant model following the proposed optimization strategy demonstrate promising financial improvement with a dynamic and intuitive dispatch planning method, emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling.

The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making. The success of these tools is subjected to the understanding of the intrinsic processes being used during the classical pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous step to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with each of these tasks, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes.

In Schizophrenia, pathway-genotypes may be constructed by combining interrelated immune biomarkers with changes in specific neurocognitive functions that represent aberrations in brain neuronal circuits. These constructs provide insight on the phenome of schizophrenia and show how pathway-phenotypes mediate the effects of genome X environmentome interactions on the symptomatology/phenomenology of schizophrenia. Nevertheless, there is a lack of knowledge how to construct pathway-phenotypes using Partial Least Squares (PLS) path modeling and Soft Independent Modeling of Class Analogy (SIMCA). This paper aims to provide a step-by-step utilization guide for the construction of pathway-phenotypes that reflect aberrations in the neuroimmune - brain circuit axis (NIBCA) in deficit schizophrenia. This NIBCA index is constructed using immune biomarkers (CCL-2, CCL-11, IL-1β, sIL-1RA, TNF-α, sTNFR1, sTNFR2) and neurocognitive tests (Brief Assessment of Cognition in Schizophrenia) predicting overall severity of schizophrenia (OSOS) in 120 deficit SCZ and 54 healthy participants. Using SmartPLS path analysis, a latent vector is extracted from those biomarkers and cognitive tests, which shows a good construct reliability (Cronbach alpha and composite reliability) and replicability and which is reflectively measured through its NIBCA manifestations. This NIBCA pathway-phenotype explains 75.0% of the variance in PHEMN (psychotic, hostility, excitation, mannerism and negative) symptoms. Using SIMCA, we constructed a NIBCA pathway-class that defines deficit schizophrenia as a qualitatively distinct nosological entity and which allows patients with deficit schizophrenia to be authenticated as belonging to the deficit schizophrenia class. In conclusion, our nomothetic approach to develop a nomological network combining neuro-immune and neurocognitive phenome markers to predict OSOS and cross-validate a diagnostic class generated replicable models reflecting the key phenome of the illness, which may mediate the effects of genome X environmentome interactions on the final outcome phenome features, namely symptomatology and phenomenology.

Machine learning (ML) methodologies have been looked upon recently as a potential candidate for mitigating nonlinearity issues in optical communications. In this paper, we experimentally demonstrate a 40-Gb/s 256-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator. By utilizing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

Biofuels construct an essential pillar of energy systems. Biofuels are considered as a popular resource for electricity production, heating, household, and industrial usage, liquid fuels, and mobility around the world. Thus, the need for handling, modeling, decision-making, demand, and forecasting for biofuels are of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biofuels production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy in modeling the biofuels.

Decision-makers are concerned with the inherent complexity of the modern world's markets. However, price fluctuations, environmental concerns, technological development, emerging markets, political challenges, and social expectations made the 21^st century's more dynamic and complex. From a policy-making perspective, it is vital to uncover future trends. This paper proposed that artificial intelligence can improve interpretations in complex markets, such as financial and energy markets. In a complex environment, it is critical to investigate maximum available input features to ensure no valuable informative feature is neglected. Some AI-based models are investigated and presented that AI-based models can successfully uncover future trends. From a scenario development perspective purified input features subset refer to driving forces which shape alternative futures. Results showed that using AI can improve our understanding of how input features influence future behaviors and simultaneously improves prediction accuracy and reliability.

Sleep postures monitoring systems in the hospital aim at transforming sensing signals into quantitative data to characterize the sleep behaviors of the patient. However, a home-care sleep posture monitoring system needs to be user friendly. In this paper, we present iSleePost - a user-friendly home-care intelligent sleep posture monitoring system. We address the labor-intensive labeling issue of traditional machine learning approaches in the training phase. Our proposed mobile health (mHealth) system leverages the communications and computation capabilities of mobile phones for provisioning a continuous sleep posture monitoring service. Our experiments show that iSleePost can achieve 90 percent accuracy in recognizing sleep postures. More importantly, iSleePost demonstrates that an easily-wear wrist sensor can accurately quantify sleep postures.

In the fashion field, the use of electroplated small metal parts such as studs, clips and buckles is widespread. The plate is often made of precious metal, such as gold or platinum. Due to the high cost of these materials, it is strategically relevant and of primary importance for manufacturers to avoid any waste by depositing only the strictly necessary amount of material. To this aim, Companies need to be aware of the overall number of items to be electroplated so that it is possible to properly set the parameters driving the galvanic process. Accordingly, the present paper describes a Machine Vision-based method able to automatically count small metal parts arranged on a galvanic frame. The devised method relies on the definition of a proper acquisition system and on the development of image processing-based routines. Such a system is then implemented on a counting machine is meant to be adopted in the galvanic industrial practice to properly define a suitable set or working parameters (such as current, voltage and deposition time) for the electroplating machine and, thereby, to assure the desired plate thickness from one side and to avoid material waste on the other.

XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims vs. no claims can be used to identify the determinants of traffic accidents. We compare the relative performances of logistic regression and XGBoost approaches for predicting the existence of accident claims using telematics data. The dataset contains information from an insurance company about individuals’ driving patterns – including total annual distance driven and percentage of total distance driven in urban areas. Our findings show that logistic regression is a suitable model given its interpretability and good predictive capacity. XGBoost requires numerous model-tuning procedures to match the predictive performance of the logistic regression model and greater effort as regards interpretation.

Longitudinal dispersion coefficient (LDC) plays an essential role in modeling the transport of pollution and sediment in the natural rivers. As a result of transportation processes, the concentration of pollution changes along the river. Different studies have been conducted to provide simple equations for estimating LDC. In this study, Support Vector Regression (SVR), Gaussian Process Regression (GPR), M5P and Random Forest (RF) examined to predict the LDC in the natural streams. The hydraulic and geometric features of different rivers gathered for developing the mentioned models for LDC estimation and various statistical criteria were utilized to scrutinize of the models. Furthermore, the Taylor chart was used to evaluate the models and achieved results showed that among machine learning models, M5P displayed the superior performance with CC of 0.823, SI of 0.812, NS of 0.577 and WI of 0.879. As well, S-D model with CC of 0.795 SI of 0.827, NS of 0.558 and WI of 0.890 had more precise results than other empirical models. The results indicates that the developed M5P model with simple formulations was superior to other machine learning models and empirical models and therefore, it can be used as a proper tool for estimating LDC in natural rivers.

Crop breeding is as ancient as the invention of cultivation. In essence, the objective of crop breeding is to improve plant fitness under human cultivation conditions, making crops more productive while maintaining consistency in life cycle and quality. The applications of predictive breeding has been gaining momentum in agricultural industry and public breeding programs for the last decade, in the aftermath of genomic selection being recognized and widely applied for accelerating genetic gain in breeding programs. The massive amounts of data that has been generated by industry and farmers year after year through several decades has finally been recognized as an asset. A wide range of analytical methods such as machine learning, deep learning and artificial intelligence that were initially developed for diverse quantitative disciplines are now being adopted to crop breeding decision making processes. New technologies are currently being developed that would enable integration of data from various domains such as geospatial variables and a multitude of phenotypic responses as well as genetic information, in order to identify, develop and improve crop faster via partial or full automation of the decisions that pertain to variety development. Here we will discuss and summarize efforts from public and private domains for predictive analytics, and its applications to crop breeding and agricultural product development, and provide suggestions for future research.

In this paper, modification of dual-Halbach permanent magnet (PM) array is investigated to improve performance of tubular linear machine, in terms of flux density and output power. Instead of a classical Halbach array with only radial and axial PMs, proposed model involves insertion of mig-magnets, which have magnetized angle shifted from the reference magnetized angles of axial and radial PMs. This structure leads to elimination of flux leakage and concentration of flux linkage in middle of the coil; therefore, the performance of machine is increased.

Oil and gas pipeline vandalism is a recurrent problem in oil rich zones of Nigeria and its West African neighbors and remains a challenge for multinationals to set ahead control measures to avert possible damages to operations both in infrastructure and business profit margins. In this paper, an integrative systems model comprising of a machine intelligence technique called Hierarchical Temporal Memory (HTM) and a sequence learning neural network called the Online-Sequential Extreme Learning Machine (OS-ELM) is proposed for monitoring and prediction of pipeline pressure data. The system models the continual prediction of pipeline oil/gas pressure signals useful for secure monitoring and control to avert acts of vandalism in oil and gas installations. The HTM uses a spatial pooler operated in temporal aggregated fashion and is defined as HTM-SP. The OS-ELM technique uses an explicit hierarchical training scheme so that the best cost estimates may be found after a stipulated number of trial runs. We study the performance of three OS-ELM neural activations: the sigmoid (sig), sinusoidal (sin) and radial basis function (rbf) activations. The results indicate improvement factors of 1.297, 1.297 and 1.300 of the HTM-SP over the OS-ELM sigmoid, sinusoidal and radial basis activations respectively.

Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research was only done in simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic applications. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable products without unnecessary heavy and uncomfortable electronic devices.

Neural microprobes represent an important component of neural prosthetic systems where implanted microprobes record the electro-potentials generated by specific thoughts in the brain and convey the signals to algorithms trained to interpret these thoughts. Here, we present novel elongated multi-site neural probe that can reach depths greater than 10mm. We hypothesize that reaching such depth allows the recording of cognitive signals required to drive cognitive prosthetics. The impedance of the recording sites on the probes was on the order of 500 kΩ at 1 kHz, which is consistent with probes used for neurophysiological recordings. The probes were made porous using Xenon Difluoride (XeF₂) dry etching to improve the biocompatibility and their adherence to the surrounding neural tissue. Numerical studies were performed to determine the reliability of the porous probes. We implanted the elongated probe in rats and show that the elongated probes are capable of simultaneously recording both spikes and local field potentials (LFPs) from various recording sites.

Global food security for the increasing world population not only requires increased sustainable production of food but a significant reduction in pre- and post-harvest waste. The timing of when a fruit is harvested is critical for reducing waste along the supply chain and increasing fruit quality for consumers. The early in field assessment of fruit ripeness and prediction of the harvest date and yield by non-destructive technologies have the potential to revolutionize farming practices and enable the consumer to eat the tastiest and freshest fruit possible. A variety of non-destructive techniques have been applied to estimate the ripeness or maturity but not all of them are applicable for in situ (field or glasshouse) assessment. This review focuses on the non-destructive methods which are promising, or have already been, applied to the pre-harvest in field measurement including colorimetry, visible imaging, spectroscopy and spectroscopic imaging. Machine learning and regression models used in assessing ripeness are also discussed.

This paper presents new above-ground biomass (AGB) and biomass components equations for seventeen forest species in the temperate forests of northwestern Mexico. A data set corresponding to 1336 destructively sampled oak and pine trees was used to fit the models. Generalized method of moments was used to simultaneously fit systems of equations for biomass components and AGB, to ensure additivity. Additionally, the carbon content of each tree component was calculated by the dry combustion method, in a TOC analyser. The fitted equations accounted for on average 91, 83, 84 and 78% of the observed variance in stem wood and stem bark, branch and foliage biomass, respectively, whereas the total AGB equations explained on average 93% of the total observed variance in AGB. The inclusion of h or d²h as additional predictor in the d-only based equations systems slightly improved estimates of stem wood, stem bark and total above-ground biomass, and greatly improved the estimates produced by the branch and foliage biomass equations. The fitted equations were used to estimate AGB stocks at stand level from a database on growing stock from 429 permanent sampling plots. Three machine-learning techniques were used to model the estimated stand level AGB and carbon contents; the selected models were applied to map the AGB and carbon distributions in the study area, which yielded mean values of 129.84 Mg ha^-1 and 63.80 Mg ha^-1, respectively.

Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific default risks; we propose to construct proxy CDS rates by associating to illiquid counterparty liquid CDS Proxy based on Machine Learning Techniques. After testing 156 classifiers from 8 most popular classifier families, we found that some classifiers achieve highly satisfactory accuracy rates. Furthermore, we have rank-ordered the performances and investigated performance variations amongst and within the 8 classifier families. This paper is, to the best of our knowledge, the first systematic study of CDS Proxy construction by Machine Learning techniques, and the first systematic classifier comparison study based entirely on financial market data. Its findings both confirm and contrast existing classifier performance literature. Given the typically highly correlated nature of financial data, we investigated the impact of correlation on classifier performance. The techniques used in this paper should be of interest for financial institutions seeking a CDS Proxy method, and can serve for proxy construction for other financial variables. Some directions for future research are indicated.

As renewable energy generation prediction systems have been introduced into the energy trading market, accurate prediction of solar photovoltaic (PV) energy generation has become a crucial challenge for ensuring stable trade of variable energy. Therefore, it is essential to quantitatively investigate and analyze current prediction technology and develop more advanced prediction systems. In this study, three models for predicting PV energy generation were investigated. Multiple regression, random forest, and gradient boosting machine (GBM) models were con-structed for 15 utility-scale power plants throughout South Korea. Model performance was evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE). The multiple regression-based model had an RMSE of 12.00% and MAE of 9.06%. The random forest model had an RMSE of 11.69% and MAE of 8.61%. The GBM model had an RMSE of 11.34% and MAE of 8.18%, indicating its superior accuracy among the three models in predicting PV energy generation. These findings provide valuable insights that may contribute to the development of advanced PV energy generation models and their application in grid operations.

This article compares the predictive capabilities of six models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), XGBoost, Random Forest (RF) and Deep Neural Network (DNN) to predict the default behaviour of credit card holders in Taiwan using data from the UCI machine learning database. Python programming language was used for data analysis. Statistical methods were compared with machine learning algorithms using the confusion matrix measured in metric terms of prediction accuracy, sensitivity, specificity, precision, G-mean, F1 score, ROC and AUC. The dataset contains 30,000 credit card user’s information with 6636 default observations and 23,364 non-default cases. The study results found that modern machine learning methods outperformed traditional statistical methods in terms of predictive performance measured by F1 score, G-mean and AUC. Traditional methods like logistic regression were marginally better than linear discriminant analysis and support vector machines in terms of predictive performance measured by area under the receiver operating characteristic curve. In the modern machine learning methods, deep neural networks were better in most of the predictive performance metrics than XGBoost and Random Forest methods.

Recently, there has been, on the one hand, an increase in the complexity of digital device designs and, on the other hand, an increase in the requirements for the development time and the reliability of the designs. One of the directions of solving this problem is developing new techniques for designing digital devices.This paper proposes a new technique for designing digital devices based on finite state machines with datapath (FSMD), when the functioning of the device is described in the form of an algorithm state machine with datapath (ASMD) charts. The new technique is called ASMD-FSMD. Different digital device design techniques are compared to each other using design examples of a synchronous multiplier on field programmable gate array (FPGA). The efficiency of the ASMD-FSMD technique compared to the traditional approach in terms of area and performance was investigated. The ASMD-FSMD technique, compared to the traditional one, reduces the area from 28.6% to 39.7% and increases the speed for some designs to 17.6%. In addition, using the ASMD-FSMD technique significantly reduces design time and increases design reliability. In conclusion, recommendations for using the ASMD-FSMD technique are made.

Objectives: Cerebral edema, a prevalent consequence of brain injury, is associated with significant mortality and disability. Timely diagnosis and monitoring are crucial for patient prognosis. There is a pressing clinical demand for a real-time, non-invasive cerebral edema monitoring method. Ultrasound methods are prime candidates for such investigations due to their non-invasiveness nature; Methods: Acute cerebral edema by permanently occluding the left middle cerebral artery (MCA) was introduced in rats. Ultrasonic echo signals were collected at nine time points over a 24-hour period to extract features from both the time and frequency domains. Concurrently, histomorphological changes were examined. We utilized support vector machine (SVM), logistic regression (LR), decision tree (DT), and random forest (RF) algorithms for classifying cerebral edema types, and SVM, RF, and feedforward neural network (FNN) for predicting the cerebral infarction volume ratio; Results: The integration of 16 ultrasonic features associated with cerebral edema development with the RF model enabled effective classification of cerebral edema types with a high accuracy rate of 97.9%. Additionally, it provided an accurate prediction of the cerebral infarction volume ratio, with an R2 value of 0.8814; Conclusions: Our proposed strategy classifies cerebral edema and predicts the cerebral infarction volume ratio with satisfactory precision. The fusion of ultrasound echo features with machine learning presents a promising non-invasive approach for the monitoring of cerebral edema.

The study of hepatitis C virus (HCV) replication in cell culture is mainly based on cloned viral isolates requiring adaptation for efficient replication in Huh7 hepatoma cells. The analysis of wild type (WT) isolates has been enabled by expression of SEC14L2 and by inhibitors targeting deleterious host factors. Here, we aimed at optimizing cell culture models to allow infection with HCV from patient sera. We used Huh7-Lunet cells ectopically expressing SEC14L2, CD81 and a GFP reporter with nuclear translocation upon cleavage by the HCV protease to study HCV replication, combined with a drug-based regimen for stimulation of non-modified wildtype isolates. RT-qPCR based quantification of HCV infections using patient sera suffered from high background in the daclatasvir treated controls. We therefore established an automated image analysis pipeline based on imaging of whole wells and iterative training of a machine-learning tool, using nuclear GFP localization as a readout for HCV infection. Upon visual validation of hits assigned by the automated image analysis the method revealed no background in daclatasvir treated samples. Thereby, infection events were found for 15 of 34 high titer HCV genotype (gt) 1b sera, revealing a significant correlation of serum titer and successful infection. We further show that transfection of viral RNA extracted from sera can be used in this model as well, albeit with so far limited efficiency. Overall, we generated a robust serum infection assay for gt1b isolates using semi-automated image analysis, which was superior to conventional RT-qPCR based quantification of viral genomes.

Climate variations in temperature and precipitation significantly impact forest productivity. Precipitation influences the physiology and growth of species, while temperature regulates photosynthesis, respiration, and transpiration. This study developed bioclimatic models to as-sess how climate change will affect the carbon density of aboveground biomass (cdAGB) in Mex-ico's coniferous forests for 2050 and 2070. We used cdAGB data from the National Forest and Soils Inventory (INFyS) of Mexico and 19 bioclimatic variables from WorldClim ver. 2.0. The best predictors of cdAGB were obtained using machine learning techniques with the 'caret' library in R. The model was trained with 80% of the data and validated with the remaining 20% using Generalized Linear Models (GLM). Current cdAGB prediction maps were generated using the best predictors. Future cdAGB was calculated with the average of three general circulation mod-els (GCMs) of future climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5), under four Representative Concentration Pathways (RCPs): 2.6, 4.5, 6.0, and 8.5 W/m². The results indicate cdAGB losses in all climate scenarios, reaching up to 15 Mg C ha-1, and could occur under the RCP 8.5 scenario by 2070 in the central region of the country. Tempera-ture-related variables are more important than precipitation variables. Bioclimatic variables can explain up to 20% of the total variance in cdAGB. The temperature in the study area is expected to increase by 2.66 °C by 2050 and 3.36 °C by 2070, while precipitation is expected to fluctuate by ±10% relative to the current values, which. could geographically redistribute the cdAGB of the country's coniferous forests. These findings underscore the need for forest management to focus not only on biodiversity conservation but also on the carbon storage capacity of these ecosys-tems.

Since the advent of artificial intelligence, it has not only transformed the way we live, but is also accelerating the transformation of production methods. Machine learning is a pivotal technology that endows computers with intelligence. The primary driving force behind its advancement is the pursuit of rapid and precise knowledge acquisition. Nevertheless, the existing enterprise risk decision support system is inadequate in terms of both timeliness and effectiveness when confronted with the task of analysing vast quantities of data. Furthermore, it lacks the capacity to assimilate the expertise of managers and to facilitate interaction in an intuitive manner.By combining a variety of machine learning models, the ensemble learning method effectively leverages the advantages of different algorithms, thereby improving the accuracy and stability of risk prediction. Each model provides a unique perspective and decision-making boundaries when dealing with complex enterprise risk data, but a single model may not be adequate when faced with a particular data set or a particular type of risk. The ensemble method overcomes the problem of bias or overfitting that may be caused by a single model by combining the prediction results of multiple models, so as to obtain a more robust prediction effect. These methods optimize the final prediction results based on how well they perform during training by giving different weights to each model. This convergence strategy significantly improves the accuracy of enterprise risk assessment and helps to provide decision-makers with more reliable data support to make more informed risk management decisions in complex business environments.

Fusobacterium nucleatum (F. nucleatum, Fn), an oral commensal that can become pathogenic, is associated with periodontitis (PD), adverse pregnancy outcomes, and colorectal cancer (CRC). MicroRNAs (miRNAs) are conserved, non-coding RNAs that play a regulatory role in gene expression and are detected in microbial infections. The aim of this study was to characterize the global microRNA expression kinetics in the mandibles of C57BL/6J mice infected with F. nucleatum for 8 and 16 weeks, and to identify miRNAs as biomarkers of the PD process using high-throughput NanoString nCounter® miRNA panels. Mice were divided into four groups: 8 weeks of Fn infection, 16 weeks of Fn infection, and their respective sham infection. Fn-infected mice at both 8- and 16 weeks showed 100% bacterial colonization on the gingival surface, along with a significant increase in alveolar bone resorption (p<0.0001) and intravascular dissemination to heart, indicating its invasive potential. Out of 577 miRNAs analyzed, seven miRNAs (miR-361-5p, miR-99b-5p) were upregulated, and two miRNAs (miR-362-3p and miR-720) were downregulated in the 8-week Fn infection group. In the 16-week Fn infection group, seven miRNAs (miR-361-5p, miR-99b-5p) were upregulated miRNAs and 13 miRNAs (miR-323-3p, miR-488) were downregulated. Notably, miRNAs such as miR-205, miR-210, miR-199a-3p which were differentially expressed (DE) in the Fn-infected mice at 8 weeks and miR-28 at 16 weeks, have been previously reported in human periodontitis. The 13 miRNAs induced by F. nucleatum (miR-361-5p) are linked to multiple malignancies, including esophageal, gastric, pancreatic, and colorectal cancers. Additionally, the DE miR-126-5p in the 8-week infection group has been identified as a potential biomarker for patients with PD and cardiovascular disease. The results indicate that F. nucleatum acts as a potent oncomicrobe, inducing several miRNAs associated with PD and linking it to systemic comorbidities. Furthermore, the study revealed similar expressions between PD-associated miRNAs and nine CRC tumor-expressed miRNAs.

Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 40s with the first abstract models of intelligent machines. Soon later in the 50s and 60s machine learning algorithms such as neural networks and decision trees ignited large enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. The renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician’s decision, and later with neural networks for the detection or classification of malignant lesions in medical images. More recently, the use of natural language processing, recurrent neural networks, transformers and generative models has both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including text analysis of electronic health records, image self-labelling and self-reporting. Thanks to its versatility and impressive results and fueled by the availability of powerful computing resources and open-source libraries, AI is one of the most promising resources for frontier research and applications in medicine.

Recommending products effectively based on historical transaction data and metadata is crucial for improving user satisfaction and increasing sales. This study presents a recommendation system evaluated using Mean Average Precision (MAP@12), leveraging a diverse dataset that includes apparel types, customer demographics, product descriptions, and images. Our approach integrates Mult-Recall methods and LightGBM Ranker, utilizing various recall strategies and advanced feature engineering techniques to enhance recommendation accuracy. By combining these advanced machine learning models and ensemble techniques, our proposed solution outperforms existing methods, providing a robust and efficient recommendation system tailored to diverse customer needs and product characteristics. This integrated approach not only addresses the challenges of data sparsity and cold start but also adapts to the dynamic nature of user preferences and market trends. The results demonstrate significant improvements in both recommendation precision and user satisfaction, highlighting the potential of our approach for real-world e-commerce applications.

In this paper, we conduct a comprehensive analysis of the RounD dataset to enhance the understanding of motion forecasting for autonomous vehicles in complex roundabout environments. We implement a trajectory prediction framework that combines Long Short-Term Memory (LSTM) networks with graph-based modules to model vehicle interactions. Our primary objective is to assess the generalizability of a standard trajectory prediction model across diverse training and testing datasets. Through extensive experiments, we analyze how varying data distributions, including different road configurations and recording times, influence the model’s prediction accuracy and robustness. This study provides key insights into the challenges of domain generalization in autonomous vehicle trajectory prediction.

The convergence of Class Incremental Learning (CIL) and Machine Unlearning (MU) is a rapidly developing field in machine learning, especially relevant in adaptive and privacy-sensitive environments like finance. CIL enables models to learn new data classes over time without losing previously acquired knowledge, while MU focuses on selectively forgetting specific data to comply with privacy laws or mitigate security risks. In this paper, we examine the theoretical foundations and practical applications of both approaches, particularly in the financial domain. We explore how these two paradigms interact and complement each other, discuss key algorithms, and present examples to illustrate their applications in areas such as portfolio management, fraud detection, and data privacy. Finally, we explore challenges and potential future directions in achieving optimal synergy between CIL and MU.

We assessed machine learning techniques (MLTs) for downscaling global climate model’s precipitation to local level in Bolivia. The skill, assumptions and uncertainty of the process are analyzed. Additionally, future projections are delivered. For that, an ensemble of 20 global climate models (GCMs) from CMIP6, with random forest (RF) and support vector machines (SVM), is used on four zones (highlands, Andean slopes, Amazon lowlands and Chaco lowlands). The downscaled series’ skill is evaluated in terms of relative errors. The uncertainty is analyzed through variance decomposition. MLTs’ skill is adequate in most cases, especially for the highlands and the Andean slopes. Moreover, RF tends to outperform SVM. Stationary assumptions are robust in the highlands and Andean slopes. Perfect prognosis assumption is poor in the highlands and Andean slopes; topographical complexity appears to be the reason. Regarding uncertainties, MLTs are the dominant source. In future projections, the highlands show shorter dry spell lengths with more frequent but less intense high rainfall events and higher annual rainfall. The Andean slopes exhibit an increased annual rainfall with a reduction in the high precipitation intensities but an increased frequency. The Amazon lowlands present a decrease in annual rainfall, and the Chaco lowlands an increase.

The Cyber Security Incident Response Team executes its critical duties in the Centralized Security Operation Centers. Its primary target is to protect organizational resources from all types of Advanced Persistent Threats (APT) and attacks while making the correct judgments at the right time. Despite the great efforts and the efficient tools, the incident response mechanism faces two significant challenges. The generation of large amounts of alerts with huge rates of false positives per unit of time and the time-consuming manual expert engagement in the post-alert decision phase. The main aim of this research study is to investigate the potential of Machine Learning techniques in solving the above challenges. This study proposes six event detection modules to identify 14 different types of malicious behavior using Splunk. The available APT dataset is extended by adding more alert types. Then, the machine learning techniques i.e., Random Forest, and XGBoost, are applied to improve the decision process and reduce the false positive alerts of the Security Information and Event Management system. The proposed model outperforms the state-of-the-art techniques by predicting an accuracy of 99.6%.

Background: Although confirmed cases of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been declining since late 2020 due to general vaccination, little research has been done regarding the impact of vaccines against SARS-CoV-2 in Spain in terms of hospitalizations and deaths. Objective: Our aim was to identify the reduction in severity and mortality of coronavirus disease 2019 (COVID-19) at a nationwide level due to vaccination. Methods: We designed a retrospective, population-based study to define waves of infection and to describe the characteristics of the hospitalized population. We also studied the rollout of vaccination and its relationship with the decline in hospitalizations and deaths. Finally, we developed two mathematical models to estimate non-vaccination scenarios using machine learning modeling (with the ElasticNet and RandomForest algorithms). The vaccination and non-vaccination scenarios were eventually compared to estimate the number of averted hospitalizations and deaths. Results: In total, 498,789 patients were included, with a global mortality of 14.3%. We identified six waves or epidemic outbreaks during the observed period. We established a strong relationship between the beginning of vaccination and the decline in both hospitalizations and deaths due to COVID-19 in all age groups. We also estimated that vaccination prevented 170,959 hospitalizations (CI 95% 77,844–264,075) and 24,546 deaths (CI 95% 2,548–46,543) in Spain between March 2021 and December 2021. We estimated a global reduction of 9.19% in total deaths during the first year of COVID-19 vaccination. Conclusions: Demographic and clinical profiles changed over the first months of the pandemic. Patients over 80 years old and other age groups obtained clinical benefit from early vaccination. The severity of COVID-19, in terms of hospitalizations and deaths, decreased due to vaccination.

Automatic Milking Systems (AMS) are revolutionizing the dairy industry by boosting herd effi-ciency, primarily through increased milk yield per cow and reduced labor costs. The performance of milking machines, whether traditional or automated, can be evaluated using advanced vacuum meters through dynamic testing. This process involves scrutinizing the system and milking rou-tine to identify critical points, utilizing the VaDia™ logger (BioControl AS, Rakkestad, NO). Vacuum recordings were downloaded and analyzed using the VaDia Suite™ software under the guidance of a milking specialist. Access to data from AMS across various manufacturers and herds facilitated a retrospective study aimed at describing and comparing key milk emission parameters for different AMS brands while identifying potential mastitis risk factors. Using proper statistical procedures of SPSS 29.1 (IBM Corp, USA), researchers analyzed data from 4,878 individual quarter milkings from cows in 48 dairy herds. Results indicated significant variability in milking pa-rameters associated to quarter milk yield and AMS brand. Notably, despite AMS standardizing teat preparation and stimulation, the study revealed a surprisingly high frequency of two major mastitis risk factors—bimodality and irregular vacuum fluctuations—occurring more frequently than in conventional milking systems. This study, one of the few comparing different AMS brands and their performance, highlights the crucial role of dynamic testing in evaluating AMS perfor-mance under real-world conditions.

In conventional Federated Learning (FL), clients work together to train a model managed by a central server, intending to speed up the learning process. However, this approach imposes significant computational and communication burdens on clients, particularly with complex models. Additionally, while FL strives to protect client privacy, the server's access to local and global models raises security concerns. To address these challenges, Split Learning (SL) separates the model into parts handled by the client and the server, though it suffers from inefficiencies due to sequential client participation. To overcome these issues, SplitFed Learning (SFL) was proposed, which combines the parallelism of FL with the model-splitting strategy of SL, enabling simultaneous training by multiple clients. Our main contribution is the theoretical analysis of SFL, which, for the first time, includes non-i.i.d. datasets, non-convex loss functions, and both full and partial client participation. We provide convergence proofs for a state-of-the-art SFL algorithm based on conventional convergence analysis assumptions for FL. Our results prove that we can recover the linear convergence rate of conventional FL for the SFL algorithm with the distinction that increasing the number of local steps or clients may not speed up the convergence in SFL.

This paper examines the utility and exploitation of non-obvious "alien" AI constructions, artificial intelligence solutions that significantly deviate from human intuition and traditional problem-solving approaches. It explores recent revelations, key characteristics, applications across various domains, and far-reaching implications for scientific research and innovation. Particular attention is given to developments in mathematical reasoning, drug discovery, and healthcare. The paper also discusses challenges in implementation, ethical considerations, and future directions, including meta-learning algorithms and human-AI collaboration frameworks. By synthesizing current research and identifying emerging trends, this paper aims to provide a basic understanding of the transformative potential of non-obvious AI solutions in reshaping scientific discovery and biomedical innovation.

Hypertension is a major risk factor for cardiovascular and cerebrovascular diseases. Previous studies have indicated that the presence of heavy metals in the air may be a risk factor for hypertension. This study examined the combined effects of heavy metals on blood pressure considering, sex differences. A health impact survey was conducted with 561 residents near waste incineration facilities in Buki-myeon, Cheongju, Korea. Blood samples were analyzed for heavy metal concentrations, and the blood pressure was measured. Multiple linear and Bayesian kernel machine regressions (BKMR) were used to assess the association between heavy metal exposure and blood pressure. Men had higher blood mercury and lead concentrations, while women had higher cadmium levels. The multiple linear regression analysis revealed no significant associations between heavy metals and systolic blood pressure. However, cadmium was significantly associated with increased diastolic blood pressure in the total group and women, whereas lead was associated with the same in men. In the BKMR analysis, the joint effect of the three metals was significantly associated with diastolic blood pressure below the 45th percentile and above the 55th percentile only in men. These findings underscore the importance of considering sex differences in environmental health studies and public health strategies.

With advancements in remote sensing technology and affordable design, uncrewed aerial systems (UAS), commonly known as drones, have become prevalent in both civil and military applications, such as agriculture, public safety, and aerial imaging. However, the rise in unlawful UAS activities, such as non-compliance with legal standards and potential terrorist attacks, has raised significant public concern, necessitating effective detection and mitigation solutions. Despite the growing importance of this issue, comprehensive and detailed examinations of existing counter-UAS solutions are lacking. To address this gap, this study conducts a bibliometric analysis and scoping review of the current literature to identify key topics and emerging trends in counter-UAS approaches. Utilizing co-word and social network analyses, the study identifies strong and weak connections between selected keywords from academic articles. This study summarizes the limitations and potential opportunities within counter-UAS research, suggesting an increasing focus on multisensory fusion and machine-learning approaches for drone detection and mitigation. Additionally, areas such as swarm drone operations, UAS traffic management (UTM), and UAS networks are identified as important but promising fields for further investigation. The findings of this study provide a foundation for enhancing air and ground safety through improved counter-UAS applications.

The advancements in social networking have empowered open expression on micro blogging platforms like Twitter. Traditional Twitter Sentiment Analysis (TSA) faced challenges due to rule-based or dictionary algorithms, dealing with feature selection, ambiguity, sparse data, and language variations. This study proposed a classification framework for Twitter sentiment data using word count vectorization and machine learning techniques to reduce the difficulties addressed on annotated sentiment-labelled tweets. Various classifiers (Naive Bayes, Decision Tree, K Nearest Neighbours, Logistic Regression, and Random Forest) were evaluated based on Accuracy, Precision, Recall, F1-score, and Specificity. Random Forest outperformed others with an Area under Curve (AUC) value of 0.96, and an Average Precision (AP) score of 0.96 in sentiment classification, especially effective with minimal Twitter-specific features.

In recent years, with the development of the Internet of Things and distributed computing, the "server-edge device" architecture has been widely deployed. This study focuses on leveraging autoencoder technology to address the binary classification problem in network intrusion detection, aiming to develop a lightweight model suitable for edge devices. Traditional intrusion detection models face two main challenges when directly ported to edge devices: inadequate computational resources to support large-scale models and the need to improve the accuracy of simpler models. To tackle these issues, this research utilizes Extreme Learning Machine for its efficient training speed and compact model size to implement autoencoders. Two improvements over the latest related work are proposed: First, to improve data purity and ultimately enhance detection performance, this study partitions the data into multiple regions based on the prediction results of these autoencoders. Second, leveraging autoencoder characteristics to investigate further the data within each region. We used the public dataset NSL-KDD to test the behavior of the proposed mechanism. The experimental results show that when dealing with multi-class attacks, the model's performance was significantly improved, the accuracy and the F1-score are improved by 3.5% and 2.9%, respectively maintaining its lightweight nature.

The combination of medical imaging and deep learning has significantly improved diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent complexity of deep learning models poses challenges in understanding their decision-making processes. Interpretability and visualization techniques have emerged as crucial tools to unravel the black-box nature of these models, providing insights into their inner workings and enhancing trust in their predictions. This survey paper comprehensively examines various interpretation and visualization techniques applied to deep learning models in medical imaging. The paper reviews methodologies, discusses their applications, and evaluates their effectiveness in enhancing the interpretability, reliability, and clinical relevance of deep learning models in medical image analysis.

Explainable Artificial Intelligence (XAI) plays a vital role in increasing transparency and trust in machine learning models, particularly when applied to tabular data which is used in domains such as finance, healthcare, and marketing. This paper presents an extensive survey of XAI techniques used with tabular data and aims to analyze recent research developments since 2021. The survey classes and describes several XAI techniques pertinent to tabular data, it identifies challenges specific to this domain, and explores potential applications and emerging trends. Future research directions are outlined, concentrating on the need for clear definitions of terminology used, data security, user-centric explanations, enhanced interaction, robust evaluation metrics, and advancements in adversarial example-based analysis. The aim is to contribute to the evolving field of XAI, and provide insights for effective, trustworthy, and transparent decision-making using tabular data.

Cellular senescence is the irreversible growth arrest subsequent to oncogenic mutations, DNA damage or metabolic insult. Senescence is associated with aging and chronic age associated diseases such as cardiovascular disease and diabetes. The involvement of cellular senescence in Acute Kidney Injury (AKI) and Chronic Kidney Disease (CKD) is not fully understood however, recent studies suggest that such patients have a higher-than-normal level of cellular senescence and accelerated aging. This study aimed to discover key biomarkers of senescence in AKI and CKD patients compared to other chronic ageing diseases as controls. We show that senescence proteins CKAP4 and PTX3 are upregulated in AKI and CKD patients compared with controls with chronic diseases suggesting they may play in a role in overall kidney disease development. Additionally, CKAP4 was found to be differentially expressed in both AKI and CKD when compared to unhealthy controls hence this biomarker could be prognostic senescence biomarker of both AKI and CKD.

Type 2 diabetes mellitus (T2DM) is a global health concern driven by factors such as obesity, sedentary behavior, and poor diet. This study uses data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS) to analyze regional and individual predictors of diabetes and predi-abetes. Employing a Bayesian multinomial mixed-effects model, we account for regional varia-bility and individual factors like age, gender, BMI, residence, wealth, education, employment, and hypertension. Our results indicate significant regional differences and associations between de-mographic and health-related factors with diabetes risk. Younger individuals and those with higher BMI are more likely to be diabetic, while hypertension significantly increases diabetes risk. We applied machine learning (ML) models, including logistic regression, decision trees, k-nearest neighbor, linear discriminant analysis, and random forest, to classify diabetic status using these predictors, assessing their accuracy through 10-fold cross-validation. Logistic regression and linear discriminant analysis demonstrated robust performance across various response distributions. Simulation studies further examined the impact of different response distributions on model performance, revealing significant differences in classification accuracies. This approach of esti-mating parameters with a Bayesian model, applying ML for prediction, and conducting simulation studies to explore various scenarios highlights the importance of integrating these methodologies for effective diabetes prediction, providing insights for public health strategies to mitigate T2DM's impact.

In manufacturing chatter is an unwanted phenomenon that can lead to product quality reduction and tool wear. Real time chatter detection is key to preventing these issues and improving overall machining efficiency. In this paper we propose an unsupervised chatter detection method using autoencoders (AE) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm that uses internal signals of Computer Numerical Control (CNC) machines. The proposed method starts by using an AE to extract features from raw internal signals collected from CNC machines. This step reduces the dimensionality of the data and captures the underlying patterns of chatter. Then the extracted features are fed into DBSCAN clustering algorithm which is a density based algorithm that groups similar data points and identifies outliers. We tested the proposed method with real world data collected from various CNC machines. The results show that our unsupervised chatter detection method has high accuracy, precision and recall, can detect chatter and distinguish it from normal machining. Also the method is robust to noise and can adapt to dynamic machining conditions. In summary our work presents an unsupervised chatter detection method using AE and DBSCAN clustering that uses internal signals of CNC machines. This method is a reliable and efficient solution for real time chatter detection so manufacturers can improve product quality, optimize machining process and reduce tool wear during machining.

Chronic kidney disease (CKD) is a growing public health issue with significant morbidity and mortality rates. Traditional diagnostics, with about 70% accuracy, often delay CKD detection, highlighting the need for more efficient methods. Recent studies indicate a potential link between CKD and auditory health, yet this relationship remains underexplored due to methodological challenges and the complexity of establishing causality. Our research utilizes data spanning from 2000 to 2020 from the National Health and Nutrition Examination Survey (NHANES), covering 12,392 participants, including 2,060 diagnosed with CKD. Through meticulous analysis employing logistic regression and Mendelian randomization, we have unearthed novel insights into the bi-directional associations between hearing impairment and CKD. Furthermore, we developed and validated a machine learning model that surpasses traditional diagnostic approaches in terms of accuracy and predictive power. These findings highlight the innovative integration of auditory examinations with demographic data to enhance CKD detection. Our approach demonstrates the potential of machine learning in transforming diagnostic methodologies, thus offering a significant advancement in the field of nephrology and public health.

The exponential growth of multimodal content across social media platforms, comprising text, images, audio, and video, has catalyzed substantial interest in artificial intelligence, particularly in multi-modal sentiment analysis (MSA). This study presents a comprehensive survey of 30 research papers published between 2020 and 2024 by eminent publishers such as Elsevier, ACM, IEEE, Springer, and others indexed in Google Scholar. Our analysis primarily focuses on exploring multimodal fusion techniques and features, with specific emphasis on the integration of text and image data. Additionally, the article offers an overview of the evolution, definition, and historical context of MSA. It delves into the current challenges and potential advantages of MSA, investigating recent datasets and sophisticated models. Furthermore, the study provides insights into prospective research directions. Notably, this review offers valuable recommendations for advancing research and developing more robust MSA models, thus serving as a valuable resource for both academic and industry researchers engaged in this burgeoning field.

Procedural and reporting guidelines frame the process of scientific practices and communications among researchers and the community at large. In the pursuit of fostering reproducibility, several methodological frameworks have been proposed by several initiatives. Nevertheless, recent studies indicate that data leakage and reproducibility are still prominent challenges. Recent studies have shown the transforming potential of incorporating the FAIR (Findable, Accessible, Interoperable and Reusable) principles in the work-flow of different context such as software and machine learning model development stages to cultivate open science. In this work, we introduce a framework to calibrate reporting guidelines against the FAIR principles in order to foster reproducibility and open science. We adapted the “Best fit” framework synthesis approach to develop the calibration framework. We propose a series of defined workflows to calibrate reporting guidelines with FAIR principles and a use case to demonstrate the process. By integrating FAIR principles with established reporting guidelines, the proposed framework bridges the gap in accommodating both FAIR metrics and reporting guidelines and benefits from advantages of these major integrated components.

Credit business is a vital part of the bank's core business, which has an extremely important impact on the bank's income and development. In the operation of credit business, credit risk assessment is particularly crucial, and accurate risk assessment can minimize risks while maximizing the bank's returns. We propose a method to optimize credit risk assessment using machine learning techniques. In this work, we employ a random forest machine learning model to process and analyze large amounts of loan application data. By using correlation analysis, information enrichment, etc., the characteristics that have the most impact on credit risk assessment are screened. Subsequently, the model was constructed using a random forest algorithm. Random forests improve the generalization ability and accuracy of the model by building multiple decision trees and introducing randomness between these trees. In the experimental analysis part, we compare the performance of various models on the German credit dataset, and the results show that the deep learning model outperforms the traditional machine learning model in most indicators, verifying the effectiveness of our method.

Classifiers face a myriad of challenges in today’s data-driven world, ranging from overfitting and high computational costs to low accuracy, imbalanced training datasets, and the notorious black box effect. Furthermore, many traditional classifiers struggle with the robust handling of noisy and missing feature values. In response to these hurdles, we present classification methods that leverage the power of feature partitioning learning and outranking measures. Our classification algorithms offer an innovative approach, eliminating the need for prior domain knowledge by automatically discerning feature intervals directly from the data. These intervals capture essential patterns and characteristics within the dataset, empowering our classifiers with newfound adaptability and insight. In addition, we employ outranking measures to mitigate the influence of noise and uncertainty in the data. Through pairwise comparisons of alternatives on each feature, we enhance the robustness and reliability of our classification outcomes. The developed classifiers are empirically evaluated on several data sets from UCI repository and are compared with well-known classifiers including k Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), Naive Bayesian (NB) and Nearest Centroid (NC). The experiments result demonstrate that the classifiers based on feature interval learning and outranking approaches are robust to imbalanced data and to irrelevant features and achieve comparably and even better performances than the well-known classifiers in some cases. Moreover, our proposed classifiers produce more explainable models whilst preserving high predictive performance levels.

This study optimizes the placement and farm layout of Vertical Axis Wind Turbines (VAWTs) using a neural fuzzy network, an advanced machine learning system, and meta-heuristic algorithms. The methodology introduces a novel approach to enhance turbine efficiency and overall farm performance. The objective is to determine the optimal configuration of vertical axis wind turbines using a neural-fuzzy system and meta-heuristic optimizers. Turbine efficiency and power generation rates are assessed through three-dimensional numerical simulations based on the Reynolds-averaged Navier-Stokes equations. This innovative design, featuring straight blades with bevel-angle vortex generators, represents a significant advancement over traditional straight-blade VAWTs. Comparative analyses demonstrate that the oblique configuration exhibits higher average output moment and power. A novel concept integrates the strengths of Darrieus VAWTs, enhancing efficiency through vortex generator integration. Simulation results are validated with empirical data, confirming the superior aerodynamic efficiency and increased power generation of the oblique configuration. Inspired by the optimal group movements of birds, this study recommends adopting the oblique arrangement for improved wind turbine performance.

The article evaluates the use of machine learning methods to predict valve plate failures in hydraulic pumps. To conduct this assessment, a laboratory setup was developed that allowed the creation of a training dataset containing both the normal operating state of the pump and the operating state with three different valve plate damages. Five different predictive models were evaluated, including three composite models based on decision trees: Random Forest, Gradient-Boosted Trees, and Rotation Forest, as well as the kNN algorithm and a neural network. The analysis showed that neural networks achieved the highest prediction accuracy, enabling a prediction accuracy level of 89%. The kNN algorithm ranked second, and tree-based algorithms performed 4% worse than the neural network. Additionally, the attribute importance analysis revealed that leak flow, pressure output together with output flow, are the most important parameters for accurate prediction.

The proliferation of Internet of Things (IoT) devices on Linux platforms has heightened concerns regarding vulnerability to malware attacks. This paper introduces a novel approach to investigating the behavior of Linux IoT malware by examining syscalls and library syscall wrappers extracted through static analysis of binaries, as opposed to the conventional method of using dynamic analysis for syscall extraction. We rank and categorize Linux system calls based on their security significance, focusing on understanding malware intent without execution.Feature analysis of the assigned syscall categories and risk ranking is conducted with statistical tests to validate their effectiveness and reliability in differentiating between malware and benign binaries. Our findings demonstrate that potential threats can be reliably identified with an F1 score of 96.86%, solely by analyzing syscalls and library syscall wrappers. This method can augment traditional static analysis, providing an effective preemptive measure to enhance Linux malware analysis. This research highlights the importance of static analysis in strengthening IoT systems against emerging malware threats.

In Kenya, reports of Rift Valley fever (RVF), one of the worst climate-sensitive zoonosis, have been common. Despite the fact that several empirical studies have demonstrated that Machine learning techniques perform better than time series models in forecasting time series data, there is little evidence of their use in predicting disease outbreaks in Africa. Recently, the literature has mentioned a number of other uses of machine learning to support intelligent decision-making in the healthcare industry and public health but there is limited knowledge on the use of the XGBoost model in the prediction of disease outbreaks. Among the Kenyan provinces, Rift valley fever cases were more pronounced in Rift valley (26.80%) and Eastern (20.60%) regions. The study explored the relationship be-tween RVF incidence and various climatic factors, such as humidity, clay content, elevation, slope, and rainfall. The strongest correlation, a meager 0.02903 for rainfall, was found in the correlation matrix, which showed weak linear relationships between various climatic factors and RVF cases. These climate variables were used to train the XGBoost model, which showed remark-able performance with an AUC of 0.8908, accuracy of 99.74%, precision of 99.75%, and recall of 99.99%. Rainfall was found to be the most important predictor in the feature importance analysis. These findings are consistent with other research showing how important weather conditions are to RVF outbreaks. According to the study's findings, the use of sophisticated machine learning models that take a variety of climatic factors into account can greatly improve RVF outbreak prediction and control.

This paper explores the utilization of ensemble models and learning analytics techniques to predict student academic performance. With the advent of educational big data, institutions are increasingly leveraging advanced analytics to gain insights into student learning patterns and optimize educational outcomes. Ensemble models, which combine the predictive power of multiple algorithms, offer a robust approach to enhance prediction accuracy. The performance of the ensemble models was analyzed and compared using the Open University Learning Analytics Dataset, which consists of sources such as demographic information, historical performance data, and engagement metrics for 23,344 students. The evaluation of various ensemble models across different classification scenarios revealed that the proposed stacking model consistently emerges as the best-performing model, excelling in both multi-class and binary classification tasks.

Objective: With just ten questions, the Berlin questionnaire (BQ) stands out as one of the simplest and most widely implemented non-invasive screening tools for detecting subjects at high risk for Obstructive Sleep Apnea (OSA), a still underdiagnosed syndrome characterized by partial or complete obstruction of the upper airways during sleep. The main aim of this study was to enhance the diagnostic accuracy of the BQ through Machine Learning (ML) techniques. Methods: A ML classifier (hereafter, ML-10) was trained using the ten questions of the standard BQ. A simplified variant of the BQ, BQ-2, which comprises only two questions out of the total of ten, was also assessed in a ML context. A 10-fold cross validation scheme was used. Ground truth was provided by the Apnea-Hypopnea Index (AHI) measured by Home Sleep Apnea Testing. Model performance was determined comparing ML-10 and BQ-2 with the standard BQ by the Receiver Operating Characteristic Curve (ROC), Area Under the Curve (AUC), sensitivity, specificity, and accuracy. Results: ML-10 demonstrated superior performance in predicting the risk for OSA compared to the standard BQ and was also capable in classifying OSA with two different AHI thresholds (AHI>=15, AHI>=30), typically used in clinical practice. Remarkably, BQ-2 was also better in sensibility to assess moderate to severe OSA (AHI≥ 15) compared to the ML-10. Conclusions: The study underscores the importance of integrating ML techniques for early OSA detection, suggesting a direction for future research to improve diagnostic processes and patient outcomes in sleep medicine.

NMR spectroscopy, which is based on the phenomenon of nuclear magnetic resonance, has been widely popularized by its application in medical imaging under the name of MRI. In the organic laboratory, the 13C NMR spectrum of a newly synthetized compound remains an essential step in elucidating its structure. For the chemist, the interpretation of such a spectrum, which is a set of chemical shift values, is made easier if he has a tool capable of predicting with sufficient accuracy the carbon shift values from the structure he intends to prepare. As there are few open source methods for accurately estimating this property, we applied our graph machine approach to build models capable of predicting the chemical shifts of carbons. For this study, we have focused on benzene compounds building an optimized model derived from training a database of 10577 chemical shifts originating from 2026 structures which contain up to ten types of atoms other than carbon, namely H, O, N, S, P, Si and halogens. It provides a training root mean squared relative error (RMSRE) of 0.5 %, i.e. a root mean squared error (RMSE) of 0.6 ppm, and a mean absolute error (MAE) of 0.4 ppm for estimating the chemical shifts of the 10k carbons. The predictive capability of the graph machine model is also compared with that of three commercial software on a data set of 171 original benzenic structures (1012 chemical shifts). The graph machine model proves very efficient in predicting chemical shifts with an RMSE of 0.9 ppm, and compares favorably with the RMSEs of 3.4, 1.8 and 1.9 ppm computed with ChemDraw, ACD and MestReNova softwares respectively. Finally, a Docker-based tool is proposed to predict the carbon chemical shifts of benzenic compounds solely from their SMILES codes.

This study introduces an integrated method for managing process risks in a Business Process Reengineering (BPR) project, using Robust Data Envelopment Analysis (RDEA) and machine learning (ML). The goal is to prioritize risks based on three standard factors of PFMEA: Severity, Occurrence, and Detection (S-O-D), and incorporating two additional factors (Breakdown Cost and Breakdown Duration) seen as undesirable outputs. The model also accounts for the effect of uncertainty on expert-estimated values by applying disturbance percentages in the linear PFMEA-RDEA model. A machine learning model is proposed to predict new values if partial or total modifications have been made to the processes. The approach was implemented in an au-tomotive sector company, and the results showed the impact of uncertainty on values by com-paring different approaches such as RPN, PFMEA-DEA, and PFMEA-RDEA. A new reduced risk categorization was achieved, who allowed for decision-makers to focus on necessary actions for reengineering.

This paper introduces an intelligent model that combines military expertise with the latest ad-vancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes. The model is based on direct input and val-idation from field decision-makers for their practical applicability and effectiveness. With a survey polling the inputs of demining experts, 95% of the responses came with an affirmation of the potential of the model to reduce threats and increase operational efficiency. It includes mili-tary-specific factors that factor in the proximity to strategic locations as well as environmental variables like vegetation cover and terrain resolution. With Gradient Boosting algorithms such as XGBoost and LightGBM, the accuracy rate is almost 97%. Such precision levels further enhance threat assessment, better allocation of resources, and around a 30% reduction in the cost and time of conducting demining operations, signifying a strong synergy of human expertise with algo-rithmic precision for maximal safety and effectiveness in demining

In recent years, extensive research has been conducted on the IoT, one of its challenges has been the field security and penetration in these networks. security solutions it Require more planning and awareness to ensure system security and privacy protection.it has also been shown that adjusting the weights of neural networks can help improve the accuracy of detection to what extent. the challenge in the attack detection field is to increase the accuracy of attack detection by machine learning methods, in this paper, a new method is presented to adjust weights of the random neural network for attack detection. Evaluations of the proposed method show that compared to random neural network methods, nearest neighbor, and support vector machine, the accuracy of attack detection improves up to 99.49 percent, and the random neural network method has been able to improve by 99.01 percent, placing the best methods together in these experiments in the form of a multi-learning model showed that the results also had an increase in accuracy up to 99.56 percent, while the training time in the proposed model is less compared to the random neural network method.

Despite the availability of COVID-19 vaccines, the global pandemic remains a significant health challenge. Timely detection of the virus is crucial for effective containment efforts, prompting the exploration of machine learning (ML) models for COVID-19 detection. highlights the ongoing significance of COVID-19 as a global health crisis despite the availability of vaccines. To address the need for timely virus detection, the study explores the efficacy of machine learning (ML) models, including DNN, Voting, Bagging, SVC_rbf, SVC_linear, SVC_polynomial, and SVC_sigmoid, in predicting COVID-19 infection probability. With a dataset comprising 4000 samples split into 3200 for training and 800 for testing, the analysis reveals that DNN and Voting models demonstrate the highest performance, achieving an impressive accuracy of 89%. These results underscore the potential of ML techniques, particularly DNN and ensemble methods like Voting, in facilitating early COVID-19 detection and contributing to effective pandemic management. The study emphasizes the importance of continued research to further refine and optimize ML-based COVID-19 detection systems.

Temporomandibular disorders (TMDs) refer to a group of conditions that affect the temporomandibular joint, causing pain and dysfunction in the jaw joint and related muscles. The diagnosis of TMDs typically involved clinical assessment through operator-based physical examination, self-reported questionnaire and imaging studies. To objectivize the measurement of TMD, this study aims at investigating on the feasibility to use machine-learning algorithms fed with data gathered from low-cost and portable instruments to discriminate the presence of TMD in adult subjects. Through this aim, the experimental protocol involved fifty participants, equally distributed between TMD and healthy subjects, acting as control group. The diagnosis of TMD has been performed by a skilled operator through the typical clinical scale. Participants underwent to a baropodometric analysis by using a pressure matrix and the evaluation of the cervical mobility through inertial sensors. Nine machine-learning algorithms belonging to support vector machine, k-nearest neighbours and decision tree were compared. The k-nearest neighbours based on cosine distance was found as the best performing, achieving performance of 0.94, 0.94 and 0.08 for the accuracy, F1-score and G-index, respectively. These findings open the possibility to use such methodology to support the diagnosis of TMDs in clinical environments.

This work focuses on short-term forecasting of PV output power. The multilayer perception (MLP), convolutional neural networks (CNN), and k-nearest neighbour (kNN) neural networks have been used singly or in a hybrid (with other algorithms) to forecast solar PV power or global solar irradiance with good success. Their performances in forecasting PV power have been compared with other algorithms but not with themselves. The study aims to compare performance of a number of neural network algorithms in solar PV energy yield forecasting under different weather conditions. The performance of MLPNN, CNN and kNN are being compared using solar PV (hourly) energy yield data for Grahamstown, Eastern Cape, South Africa. The choice of location is part of the study parameters to provide insight into renewable energy power integration in specific areas in south Africa that may be prone to extreme weather conditions. The kNN algorithm was found to have RMSE value of 4.95% and MAE value of 2.74% at its worst performance, and RMSE value of 1.49% and MAE value of 0.85% at its best performance. It outperformed the others by a good margin and kNN could serve as a fast, easy, and accurate tool to forecast solar PV output power.

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.

Lettuce (Lactuca sativa) is a leafy vegetable that provides a valuable source of phytonutrients for a healthy human diet. Assessment of plant growth and composition is vital for determining crop yield and overall quality, however, classical laboratory analyses are slow and costly. Therefore, new, less expensive, more rapid, and non-destructive approaches are being developed, including those based on (hyper)spectral reflectance. Additionally, it is also important to determine how plant phenotypes respond to fertilizer treatments and whether these differences in response can be detected from analyses of hyperspectral image data. In the current study, we demonstrate the suitability of hyperspectral imaging in combination with machine learning models to estimate the content of chlorophyll (SPAD), anthocyanins (ACI), glucose, fructose, sucrose, vitamin C, β-carotene, N, P, K, dry matter content, and plant fresh weight. The implemented five classification and regression machine learning models showed high accuracy in classifying the lettuces by the applied fertilizers treatments and estimating nutrient concentrations. To reduce the input (predictor data, i.e., hyperspectral data) dimension, 13 principal components were found and applied in models. The implemented artificial neural network models of the machine learning algorithm demonstrated high accuracy (r = 0.85 ... 0.99) in estimating fresh leaf weight, and contents of chlorophyll, anthocyanins, N, P, K, and β-carotene. The four applied classification models of machine learning demonstrated 100% accuracy in classifying the studied baby leaf lettuces by phenotype when certain fertilizer treatments were applied.

Although composting has many advantages in the treatment of organic waste, there are still many problems and challenges associated with emissions, like NH3, VOCs, and H2S, as well as greenhouse gases such as CO2, CH4, and N2O. One promising approach to enhancing composting conditions is used of novel analytical methods bad on artificial intelligence. To predict and optimize the emissions (CO, CO2, H2S, NH3) during composting process kinetics thought mathematical models (MM) and machine learning (ML) models were utilized. Data about everyday emissions from laboratory composting with compost’s biochar with different incubation (50, 60, 70 °C) and biochar doses (0, 3, 6, 9, 12, 15% d.m.) were used for MM and ML models selections and training. MM has not been very effective in predicting emissions, (R2 0.1 - 0.9), while ML models such as acritical neural network (ANN, Bayesian Regularized Neural Network; R2 accuracy CO:0,71, CO2:0,81, NH3:0,95, H2S:0,72)) and decision tree (DT, RPART; R2 accuracy CO:0,693, CO2:0,80, NH3:0,93, H2S:0,65) have demonstrated satisfactory results. For the first time the ML models to predict CO and H2S during composting were demonstrated. Further research in a semi-scale and field study composting with biochar is needed to improve the accuracy of developments models.

The dawn of Artificial Intelligence (AI) in healthcare stands as a milestone in medical innovation. A plethora of different medical fields are heavily involved and pediatric emergency medicine is no exception. These new tools do not merely provide more advanced and efficient systems for patients’ diagnosis, management and treatment. They rather concern a strict shift from traditional methods based upon broad categories towards a more personalized healthcare. AI offers many promises in pediatric emergency medicine with a wide range of applications involving clinical decision making, patients’ flows management and prioritization. Main barriers to a widespread diffusion involve technological challenges but also ethical issues and the paucity of extensive datasets in pediatric contexts. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies, pointing out the actual applications and challenges until future feasible research perspectives.

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.

Analysis of land use/land cover (LULC) in the catchment areas is the first action toward safeguarding the freshwater resources. The LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), such as Random Forests (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Naïve Bayes (NB) was investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs were based on the visual inspection of the analyst and the commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and kappa coefficient. The MLAs produced good results, where RF (OA= 97.02%, Kappa= 0.96), SVM (OA= 89.74 %, Kappa= 0.88), ANN (OA= 87%, Kappa= 0.86), and NB (OA= 68.64 Kappa= 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a small margin. While NB yielded satisfactory results, which could be primarily influenced by its sensitivity to limited training samples. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.

The opportunities and advantages of implementing Artificial Intelligence (AI) and Machine Learning (ML) to optimize the process control of wastewater treatment in the Oil and Gas industry are discussed in this work. Discussion is centered around five major points: 1) Various types of Oil and Gas wastewater (Sour, Oily, Produced) and industry standards/methods for treatment, including specific case studies and challenges when treating certain wastewater. This section aims at the common types of wastewater in the Oil and Gas industry and how they are generally treated. 2) How AI/ML has been implemented in wastewater treatment in various industries; summary of which AI/ML languages were used along with a brief overview of how they work. This article focused on an understanding of the specific case studies and AI explanations to understand the effectiveness of wastewater treatment using AI/ML. 3) AI that is currently implemented in Oil and Gas such as personal protective equipment (PPE), smartwatches radiofrequency identification etc. This showed that AI/ML already plays a successful role in the Oil and Gas industry and has proven to help optimize many processes and components. Many of these AI/ML implementations are now industry standards and have greatly benefitted the industry. This section demonstrates that the implementation and the use of AI/ML have been proven to be a successful endeavor and that expanding the role of AI/ML would be beneficial. 5) Using the established points from the previous sections the paper outlines why AI/ML is ideal for Oil and Gas wastewater treatment; this argument centers around fit-for-purpose treatment, and the possible uses of the treated water. A determination of the best-suited AI languages, possible implementation scenarios, and how this would shift the way Oil and Gas industries treat and approach wastewater is presented to the reader.

Deep learning is widely used in image segmentation, effectively identifying crops and weeds and contributing to the reduction of herbicide use. Traditional methods of applying pesticides over large areas are inefficient in dealing with weeds as needed, leading to massive waste of pesticides, high-cost production, and serious environmental pollution. This affects crop yield and quality. Although weed recognition using conventional deep learning methods has evolved over time, there are still challenges in weed extraction, detection, and segmentation. Accurate recognition and detection of weeds are essential prerequisites for implementing variable spraying. This paper proposes a semantic segmentation method based on UNet++ for complex environments where accurate identification of plants and weeds is challenging, targeting weeds in sugar beets, peas, and rice. An attention module is integrated into the upsampling process of UNet++, and UNet++ is used as a backbone network to effectively integrate multi-scale information, efficiently suppressing external noise interference. The UNet++ model with an integrated attention mechanism module achieves higher IOU than the general UNet++ model used in medical image analysis. This method effectively detects crops and weeds in complex backgrounds, providing reference material for the accurate application of robotic herbicides.

The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of lake Nokoue in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Lake Nokoue, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

INTRODUCTION : In this scoping review, we delve into the transformative potential of artificial intelligence (AI) in addressing challenges inherent in whole genome sequencing (WGS) analysis, with a specific focus on its implications in surgical oncology. METHODS: Scoping review of whole genomic sequencing and artificial intelligence.DISCUSSION : Unveiling the limitations of existing sequencing technologies, the review illuminates how AI-powered methods emerge as innovative solutions to surmount these obstacles. The evolution of DNA sequencing technologies, progressing from Sanger sequencing to next-generation sequencing, sets the backdrop for AI's emergence as a potent ally in processing and analyzing the voluminous genomic data generated by these technologies. Particularly, deep learning methods play a pivotal role in extracting knowledge and discerning patterns from the vast landscape of genomic information. In the context of oncology, AI-powered methods exhibit considerable potential across diverse facets of WGS analysis, including variant calling, structural variation identification, and pharmacogenomic analysis. CONCLUSIONS : This review underscores the significance of multimodal approaches in diagnoses and therapies, highlighting the imperative for ongoing research and development in AI-powered WGS techniques. Integrating AI into the analytical framework empowers scientists and clinicians to unravel the intricate interplay of genomics within the realm of multi-omics research, paving the way for more personalized and targeted treatments in surgical oncology and perhaps beyond.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

Heavy metal pollution poses a significant threat to ecosystems and human health worldwide. In response to this pressing issue, bioremediation has emerged as a promising approach for mitigating contamination. This paper adopts a bibliographic method to explore the key techniques and applications of bioremediation. Furthermore, we delve into the most prominent threats to human environmental health and the corresponding remediation methods. Additionally, we discuss the future trajectory of bioremediation research, with a particular focus on the integration of big data and machine learning technologies. These advanced methodologies hold great potential for enhancing the effectiveness and efficiency of environmental remediation efforts in the face of escalating pollution challenges.

Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expen-sive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in Ceará, Brazil. Several machine learning models were trained and tested, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and sup-port vector machine models. A stepwise approach determined the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH, achieving an R2 of 0.91, MAPE of 102.34%, and RMSE of 20.38 g/L, which are values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.

A compositional data vector is a special type of multivariate observation in which the elements of the vector are non-negative and sum to a constant, usually taken to be unity. A compositional data does not have zero and only retains relative information. Furthermore, comparisons can only be made between compositional data of the same component. The relevant sample space is the standard simplex. A simplex space is a space that is a generalized form of a triangle. For compositional data, many of the operations defined in Euclidean space are meaningless. Microbiome analyzes have become popular in recent years. Operational Taxonomic Units (OTU) and Amplicon Sequence Variants (ASVs) used in microbiome analysis are one type of compositional data. The microbiome data are counts of different species within a sample and it is compositional. Although there is an order of bacterial abundance within a sample, there is no order of bacterial abundance between samples. Firmicutes and Bacteroidetes, the major phyla in the colon, have been observed in humans worldwide. Gut microbiome analyses often use the Firmicutes/Bacteroidetes ratio and principal coordinates analysis (PCoA). Alpha and beta diversities are used as indicators of bacterial flora diversity. However, misinformation is pervasive in the human microbiome literature and analysis. There is a lot of misunderstanding regarding compositional data and its analysis. An attempt was made to demonstrate how to analyze using the “National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN JMD) (Public Data).” The results showed that PCoA did not work, and principal component analysis (PCA) was useful for analyzing the gut microbiome relative diversity.

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the high-quality reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of comprehensive in situ data together with high-resolution hyperspectral imagery. We showcase the capabilities of this team using data collected in a north Texas pond during three collections in the fall of 2020. Machine learning models for 13 variables are trained using the data set of paired in situ measurements and coincident reflectance spectra. These models can successfully estimate physical variables, including temperature, conductivity, pH, and turbidity, as well as the concentrations of blue-green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners and the ions Ca2+, Cl−, and Na+. We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. The maps generated by applying each trained model to the collected images reveal the small-scale spatial variability within the pond. Additionally, the permutation importance computed for each feature of the trained models highlights the spectral features relevant to each water quality variable.

The paper concerns with the identification of current communication technologies and software functionalities from the field of digitalization and Industry 4.0, from the point of view of benefits and applicability for the machine manufacturer. In order to identify the relevant technologies and functionalities, both a descriptive and statistical literature research was carried out. As a result, both the currently most widespread technologies and eleven IT functionalities relevant to mechanical engineers were identified and described in terms of their application and implementation. In addition, a knowledge gap in the area of industry transfer in the field of Industry 4.0 / Digitalization was identified

Background: The growth of arthroplasty procedures requires innovative strategies to reduce inpatients Length of Stay (LOS). This study aims to develop a machine learning prediction model that may aid to predict LOS after hip or knee arthroplasties. Methods: A collection of all the clinical notes of patients undergoing elective primary or revision arthroplasty from 1 January 2019 to 31 December 2019 was performed. The hospitalization has been classified as “Short LOS” if it was less than or equal to 6 days and “Long LOS” if it was greater than 7 days. Clinical data coming from pre-operative laboratory analysis, vital parameters, demographic characteristics of patients were screened. Final data have been used to train a Logistic Regression model with the aim of predicting short or long LOS. Results: Final dataset was composed of 1517 patients (795 “LONG”, 722 “SHORT”, p = 0.3196) with a total of 1541 admissions (729 “LONG”, 812 “SHORT”, p &lt; 0.000). Complete model had a prediction efficacy of 78,99% (AUC 0.7899). Conclusions: Machine learning may facilitate day-by-day clinical practice predicting which patients are suitable for a shorter LOS, from those with a longer LOS in which a cautious approach could be recommended.

Machine learning (ML) based on neural network (NN) facilitates data-driven techniques for handling large amounts of data, either obtained through experiments or simulations at multiple spatio-temporal scales, thereby finding the hidden patterns underlying these data and promoting efficient research methods. The main purpose of this paper is to extend the capabilities of a new solver, called realFluidReactingNNFoam, under development at University of Perugia in OpenFOAM with NN algorithm for replacing complex real-fluid thermophysical property evaluations, using the approach of coupling OpenFOAM and Python-trained NN models. Currently, NN models are trained against data generated using the Peng-Robinson equation of state (PR-EoS) assuming mixture frozen temperature. The OpenFOAM solver, where needed, calls the NN models in each grid-cell with appropriate inputs, and the returned results are used and stored in suitable OpenFOAM data structures. Such inference for the thermophysical properties is achieved via the “Neural Network Inference in C made Easy” (NNICE) library, which proved to be very efficient and robust. The overall model is validated considering a liquid-rocket benchmark comprised of liquid-oxygen (LOX) and gaseous-hydrogen (GH2) streams. The model accounts for real-fluid thermodynamics and transport properties, making use of the PR-EoS and the Chung transport models. First, the development of a real-fluid model (RFM) with artificial neural network is described in detail. Then, the numerical results of the transcritical mixing layer (LOX/GH2) benchmark are presented and analyzed in terms of accuracy and computational ef-ficiency. Results of the overall implementation indicate that the combined OpenFOAM and ML approach provides a speed-up factor higher than seven, while preserving the original solver accuracy.

Convolutional neural networks (CNNs) have proven to be a very efficient class of machine learning (ML) architectures for handling multidimensional data by maintaining data locality, especially in the field of computer vision. Data pooling, a major component of CNNs, plays a crucial role for extracting important features of the input data and downsampling its dimensionality. Multidimensional pooling, however, is not efficiently implemented in existing ML algorithms. In particular, quantum machine learning (QML) algorithms have a tendency to ignore data locality for higher dimensions by representing/flattening multidimensional data as simple one-dimensional data. In this work, we propose using the quantum Haar transform (QHT) and quantum partial measurement for performing generalized pooling operations on multidimensional data. We present the corresponding decoherence-optimized quantum circuits for the proposed techniques along with their theoretical circuit depth analysis. Our experimental work was conducted using multidimensional data, ranging from 1-D audio data, to 2-D image data, to 3-D hyperspectral data, to demonstrate the scalability of the proposed methods. In our experiments, we utilized both noisy and noise-free quantum simulations on a state-of-the-art quantum simulator from IBM Quantum. We also show the efficiency of our proposed techniques for multidimensional data by reporting the fidelity of results.

Online learning is gaining massive popularity with time. The e-learning platforms operate differently from traditional educational institutions and hence need different strategy for course recommendations. This survey aims to cover the major emerging research areas in e-learning recommender systems. Our study covers different areas of research including graph-based methodologies, ITS, query optimization, content-based, and collaborative filtering, big data, and association rules mining. This survey aimed to explore all major emerging directions of recommender systems in education. This study analyzed existing literature in all of the areas mentioned before and performed objective-based analysis. A brief performance analysis was also done for the researches where the values were comparable. Limitations of existing researches were also identified and studied to shed some light on future directions.

Hydroxyapatite and b-tricalcium phosphate have been clinically applied as artificial bone materials due to their high biocompatibility. The development of artificial bones requires the verification of safety and efficacy through animal experiments; however, from the viewpoint of animal welfare, there is necessary to reduce the number of animal experiments. In this study, we utilized machine learning to construct a model that estimates the bone-forming ability of bioceramics from material fabrication conditions, material properties, and in vivo experimental conditions. We succeeded in constructing the two models as follows: ‘Model 1’, which predicts material properties from their fabrication conditions, and ‘Model 2’, which predicts the bone-formation rate from material properties and in vivo experimental conditions. By an inverse analysis of the two models, we proposed candidates for material fabrication conditions to achieve target values of bone-formation rate. Under the proposed conditions, the material properties of the fabricated material were consistent with the estimated material properties. Furthermore, a comparison between bone-formation rates after 12 weeks of implantation in the porcine tibia and the estimated bone-formation rate. This result showed that the actual bone-formation rates existed within the error range of the estimated bone-formation rates, indicating that machine learning consistently predicts the results of animal experiments using material fabrication conditions. We believe that these findings will lead to the establishment of alternative animal experiments to replace animal experiments in the development of artificial bones.

The effect of climate change plays a significant role on groundwater level variations and crop yield. The change in climate leading to increased temperatures, decreased rainfalls and extreme drought conditions ultimately cause low groundwater level and crop yield. Judicious groundwater management technology viz., water recharge options, need based irrigation, crop selection, must be adapted for further increasing the water table level in soil ecosystem. Additionally, climate change impacts combined with changes in agricultural water use can affect groundwater dynamics. Increased irrigation demand and decreased summer precipitation can lower groundwater levels, impacting crop production. Overall, climate change affects both groundwater resources and crop yield highlighting the need for sustainable water management practices and consideration of soil properties in agricultural modelling. The authors carefully selected relevant research articles addressing the impact of climate change on ground water level in Asian countries. The review highlights the use of machine learning method to ground water level and crop yield modelling. According to the study, machine learning techniques have made significant contributions to predicting groundwater level changes and crop yield with higher accuracies, high performance and less running time.

The World Health Organization (WHO) has released reports indicating that heart disorders hold the unfortunate distinction of being the primary cause of death worldwide. Shockingly, an astonishing estimated 17.9 million lives are claimed by heart diseases annually, accounting for an alarming 31% of all global deaths. With all the flaws in the clinical situation, it is frequently challenging to assess the severity of cardiac disease and forecast its course of progression due to heterogeneity and complex interplay of factors. Therefore, early heart disease detection is essential for effective therapy. To address these challenges, Machine Learning (ML) boosting algorithms play a pivotal role as the main components of predictive analytics required to do this. The main objective of this study is to develop a comprehensive comparative framework to predict heart diseases using state-of-the-art machine learning with boosting techniques such as Decision Tree, Random Forest, Gradient Boosting, Catboost, XGboost, Light GBM, and Adaboost. To evaluate the performance of the models, a large heart disorders dataset is used from the UCI machine learning repository, comprised of 26 feature-based numerical and categorical attributes with 8763 samples from all over the globe. The Experimental results reveal that AdaBoost attained the highest accuracy of 95% and outperforms other algorithms concerning various performance measures like precision= 0.98, recall= 0.95, specificity= 0.95, f1-score=0.01, Negative predicted value= 0.83, False positive rate= 0.04, False negative rate= 0.04 and False Development rate= 0.01.

Through the refinement of approaches to applying visual augmentation to virtual environments, the possibilities of utilising this process in robotics continues to be explored. However these endeavours have only yielded a collaborative relationship between the two technologies, where the augmented reality (AR) component is separate from the robot functionality. Regarding telepresence robots, research has shown that their perceived usefulness wanes in comparison to that of video conferencing tools as they do not offer a level of immersion and modified visual experience to justify their use. Therefore this paper contributes a novel virtually augmented robot (VAR) model that embeds AR functionality into telepresence, through the use of augmentation and facial recognition sub-models and algorithms. Experimental results show that using pulse width modulation (PWM), duty cycles, digital signals and accelerometer angles provides an approach for stabilising robot motion and the transference of human to robot head movement. The results also show the benefit of having a robot model with intrinsic AR functionality, as it offers a greater level of immersion and visual control of an environment.

The critical enzyme Dihydrofolate Reductase-Thymidylate Synthase in Leishmania major (LmDHFR-TS) serves a dual-purpose role and is essential for DNA synthesis, a cornerstone of the parasite's reproductive processes. Consequently, the development of inhibitors against LmDHFR-TS is crucial for the creation of novel anti-Leishmania chemotherapies. In this study, we employed an in-house database containing 314 secondary metabolites derived from cinnamic acid that occurred in the Asteraceae family. We conducted a combined ligand/structure-based virtual screening to identify potential inhibitors against LmDHFR-TS. By consensus analysis of both approaches, we identified three compounds, i.e., lithospermic acid (237), diarctigenin (306), and isolappaol A (308), that exhibited a high probability of being inhibitors according to both approaches and were consequently classified as promising hits. Subsequently, we expanded the binding mode examination of these compounds within the active site of the test enzyme through molecular dynamics simulations, revealing a high degree of structural stability and minimal fluctuations in its tertiary structure. The in-silico predictions were then validated through in-vitro assays to examine the inhibitory capacity of the top-ranked naturally-occurring compounds against LmDHFR-TS recombinant protein. The test compounds effectively inhibited the enzyme with IC50 values ranging from 6.1 to 10.1 μM. In contrast, other common cinnamic acid derivatives (i.e., flavonoid glycosides) from the Asteraceae family, such as hesperidin, isovitexin 4′-O-glucoside, and rutin, exhibited low activity against this target. The selective index (SI) for all tested compounds was determined using HsDHFR, with moderate inhibitory effect. Among these hits, lignans 306 and 308 demonstrated the highest selectivity, displaying superior SI values compared to methotrexate, the reference inhibitor of DHFR-TS. Therefore, continued research into the anti-leishmanial potential of these C6C3-hybrid butyrolactone lignans may offer a brighter outlook for combating this neglected tropical disease.

There is a significant risk of injury in sports and intense competition due to the demanding physical and psychological requirements. Hamstring strain injuries (HSI) are the most prevalent type of injury among professional soccer players and are the leading cause of missed days in the sport. These injuries stem from a combination of factors, making it challenging to pinpoint the most crucial risk factors and their interactions, let alone find effective prevention strategies. Recently, there has been a growing recognition of the potential of tools provided by artificial intelligence (AI). However, current studies primarily concentrate on enhancing the performance of complex machine learning models, often overlooking their explanatory capabilities. Consequently, medical teams encounter difficulty interpreting these models and are hesitant to trust them fully. In light of this, there is an increasing need for advanced injury detection and prediction models that can aid doctors in diagnosing or detecting injuries earlier and with greater accuracy. Accordingly, this study aims to identify biomarkers of muscle injuries in professional soccer players through a biomechanical analysis, employing several ML algorithms, such as Decision tree (DT) methods, Discriminant methods, Logistic regression, Naive Bayes, Support vector machine (SVM), K-nearest neighbor (KNN), Ensemble methods, Boosted and bagged trees, Artificial Neural Networks (ANN), and XGBoost. In particular, XGBoost was also used to obtain the most important features. The findings highlight that the variables that most effectively differentiate the groups and could serve as reliable predictors for injury prevention are the maximum muscle strength of the hamstrings and the stiffness of the same muscle. With regards to the 35 techniques employed, a precision of up to 78% was achieved with XGBoost, indicating that by considering scientific evidence, suggestions based on various data sources, and expert opinions, it is possible to attain good precision, thus enhancing the reliability of the results for doctors and trainers. Furthermore, the obtained results strongly align with the existing literature, although further specific studies about this sport are necessary to draw a definitive conclusion.

The aims of this paper can be traced back to goals number seven, twelve and thirteen introduced by Agenda 2030, namely "affordable and clean energy", “responsible consumption and production” and "climate action”, respectively. This is due to the fact that the present work supports decarbonization processes acting in a direct way on the electricity consumption achieved by plants pertaining to the waste industry; in fact, this study aims at the realization of a machine learning model for predicting the energy consumption achieved by a mechanical biological treatment (MBT) waste plant located in central Italy, given the distribution of the entering waste. This model is implemented in MATLAB. The model can be used to tune the distribution of the entering waste in order to adapt the plant energy consumption to the capability of the energy sources and can serve as a play-ground model for other energy transformation plants. The results of the study, which feed the literature on the application of artificial intelligence to real industrial plants, could be used to determine energy efficiency actions that could be incorporated into Property’s strategic planning.

This study utilizes the Random Forest technique to investigate risk management practices and concerns in Islamic banks using survey data from 2016 to 2021. Findings reveal that larger banks provide more consistent survey responses, driven by their confidence and larger survey budgets. Moreover, a positive link is established between a country's development, characterized by high GDPs and low inflation and interest rates, and the precision of Islamic banks' survey responses. Analyzing risk-related concerns, the study notes a significant reduction in credit portfolio risk, attributed to improved risk management practices, global economic growth, stricter regulations, and diversified asset portfolios. Concerns related to terrorism financing and cybersecurity risks have also decreased due to better enforcement of anti-money laundering regulations and investments in cybersecurity infrastructure and education. This research enhances our understanding of risk management in Islamic banks, highlighting the impact of bank size and country development. Additionally, it emphasizes the need for ongoing analysis beyond 2021 to account for potential COVID-19 effects and evolving risk management and regulatory practices in Islamic banking.

Material Extrusion is a popular additive manufacturing technology due to its low cost, wide market availability, ability to construct complex parts, safety, and cleanliness. However, optimizing the process parameters to obtain the best possible mechanical properties has not been extensively studied. This paper aims to develop ensemble learning-based models to predict the ultimate tensile strength, Young's modulus, and strain at break of PLA and PLA-CF 3D-printed parts, using printing temperature, printing speed, and layer thickness as process parameters. Additionally, the study investigates the impact of process parameters and material selection on the mechanical properties of the printed parts and uses Genetic Algorithms for multi-objective optimization according to user specifications. The results indicate that process parameters and material selection significantly influence the mechanical properties of the printed parts. The Genetic Algorithm successfully identifies optimal parameter values for the desired mechanical properties. Moreover, this work is the first to model Process-Structure-Properties relationships for an additive manufacturing process and to use a multi-objective optimization approach for multiple mechanical properties, utilizing ensemble learning-based algorithms and Genetic Algorithms.

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.

Human Action Recognition is widely used in multiple fields to recognize activities and extract spatial and temporal information. This paper focuses on analyzing various methods as well as provides extensive knowledge of the foundational concepts of the HAR. A dataset is also very crucial for any research study so we discussed the popular dataset and its features in this paper.

It is a challenging problem that air-written Arabic letters has received a lot of attention in the past decades when compared to commonly spoken languages like English languages. To fill this gap, we propose a strong model that brings together machine learning (ML) and optical character recognition (OCR) methods. The model applied several ML methods, (i.e., Neural Networks (NN), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), with deep convolutional neural networks (CNNs) such as VGG16, VGG19, and SqueezeNet for effective feature extraction. Our study utilizes the AHAWP dataset, which consists of varied writing styles and variations in hand signs, to train and evaluate the model. Preprocessing systems are applied to improve data quality by reduction bias. Besides, OCR methods are combined into our model to sequestrate individual letters from continuous air-written gestures and refine recognition results. Results of this study show that the proposed model has achieved the extreme accuracy of 88.8% using NN with VGG16. This study presents an inclusive approach that combines ML, deep CNNs, and OCR methods to address the issue of Arabic in air writing recognition research.

The prevalence of chronic diseases is dramatically increasing demand for emergency healthcare. Existing systems rely on patients self-identifying symptoms, causing dangerous delays. This study develops an AI and IoT-powered “digital twin” solution to enable continuous real-time monitoring and timely prediction of diverse medical emergencies. A digital twin is a virtual representation of an individual, modeled using multidimensional physiological data from wearable sensors. Machine learning techniques analyze patterns in this data to identify anomalies and predict emergencies like heart attacks or falls. A key contribution is an optimized ensemble algorithm combining gradient boosted trees, neural networks, and other techniques to accurately detect emergency events. Evaluation on a dataset of 9158 samples shows the digital twin identifies key emergencies with over 90% recall, enabling prevention and rapid response. It allows risk stratification and personalized interventions based on early warnings, circumventing over 2 million avoidable emergency room visits annually. This study demonstrates the feasibility of an integrated, predictive, patient-centric emergency response system enabled by digital twin technology.

Computational Fluid Dynamics (CFD)-based simulation has been the traditional way to model complex industrial systems and processes. One very large and complex industrial system that has benefited from CFD-based simulations is the steel blast furnace. The problem with the CFD-based simulation approach is that it tends to be very slow to generate data. The CFD-only approach may not be fast enough for use in real-time decision-making. To address this issue, in this work, the authors propose the use of machine learning techniques to train and test models based on data generated via CFD simulation. Regression models based on neural networks are compared to tree boosting models. In particular, several areas (tuyere, raceway, and shaft) of the blast furnace are modeled using these approaches. The results of the model training and testing are presented and discussed. The obtained R2 metrics are, in general, very high. The results look promising and may help to improve the efficiency of operator and process engineer decision-making when running a blast furnace.