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Poverty has always been a global concern that has restricted human development. The first goal (SDG 1) of the United Nations Sustainable Development Goals (SDGs) is to eliminate all forms of poverty all over the world. The establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG 1. This paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development Framework. The temporal and spatial distribution characteristics of China's poverty areas and their SDG 1 evaluation values in 2012, 2014, 2016, and 2018 have been analyzed. Based on the SDGs global indicator framework, this paper first constructed SDG 1 China's district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images, land cover data, and digital elevation model data. Secondly, we establish SDG 1 China's localized partial least squares estimation model and SDG 1 China's localized machine learning estimation model. Finally, we analyze and verify the spatiotemporal distribution characteristics of China's poverty areas and counties and their SDG 1 evaluation values. The results show that SDG 1 China's district and county localization indicator system proposed in this study and SDG 1 China's localized partial least squares estimation model can better reflect the poverty level of China's districts and counties. The estimated model R2 is 0.65, which can identify 72.77% of China's national poverty counties. From 2012 to 2018, the spatial distribution pattern of SDG evaluation values in China's districts and counties is that the SDG evaluation values gradually increase from western China to eastern China. In addition, the average SDG 1 evaluation value of China's districts and counties increased by 23% from 2012 to 2018. This paper is oriented to the United Nations SDGs framework, explores the SDG 1 localized evaluation method of China's districts and counties based on multisource remote sensing data, and provides a scientific and rapid regional poverty monitoring and evaluation program for the implementation of the 2030 agenda poverty alleviation goals. [ABSTRACT FROM AUTHOR]

The scarcity of public resources and environmental pollution caused by rapid urbanization highlight the practical significance of parks in ensuring the sustainable development of a city. Therefore, the social equity of parks warrants further study. This paper proposes a fine-grained comprehensive evaluation framework that combines geographic accessibility models, geo-statistical analysis, and machine learning algorithms to explore social inequity in Taiyuan, China. In this framework, gini coefficient and lorentz curve express spatial equality, accessibility shows spatial equity, and ridge regression model handles the interdependence of variables with different dimensions to quantify the relative effects of local participants on changes in park accessibility. On this basis, the imbalance between vulnerable groups and park supply is analyzed to further understand the core concept of social equity. Results highlight serious spatial inequality in all three types of parks allocation of six urban areas, especially in community parks. The actual access level of people to parks is also stratified by their demographic and socioeconomic characteristics, revealing the social inequity in access to parks. Park distribution is indeed not conducive to some social vulnerable groups, whose contradiction between supply and demand is highly prominent in urban – rural junctions and new urban areas. This paper also confirms the unfair layout of public facilities can be observed in second-tier cities of China by highlighting the social inequity of parks in Taiyuan. The findings of this work have profound implications for urban planning and sustainable development. [ABSTRACT FROM AUTHOR]

Ethereum is a major public blockchain. Besides being the second-largest digital currency by market capitalization for its cryptocurrency, the Ether (Ξ), it is also the foundation of Web3 and decentralized applications, or DApps, that are fuelled by Smart Contracts. At the time of this writing, Ethereum still uses Proof of Work (PoW) consensus algorithm to ensure the integrity of the blockchain and to prevent double spend. PoW requires the participation of miners, who are incentivized to assemble blocks of transactions by being rewarded with cryptocurrency paid by transaction originators and by the blockchain network itself via newly minted Ξ. Network fees for transaction submissions are called gas, by analogy to the fuel used by cars, and are negotiable. They are also highly volatile and hence it is critical to predict the direction they are heading into, so that one can time transaction submissions, when feasible. There have been several efforts to predict gas prices, including usage of large Mempools, analysis of committed blocks, and more recent ones using Facebook's Prophet model [Taylor, S. J., & Letham, B. (2017). Forecasting at scale. PeerJ Preprints, 5, e3190v2. ]. In this study, we introduce an innovative approach that employs the DeepAR [Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. ] model, known for its superior forecasting accuracy over conventional methods by virtue of its ability to learn from multiple related time series. This methodology not only offers immediate advantages but also holds promise for ongoing enhancements. We substantiate our claims through empirical testing, utilizing data extracts from the Ethereum blockchain and cryptocurrency price feeds. This document is an extended version of our ICCS 2022 paper on the same topic. In this paper, we dive deeper into the internals of DeepAR forecasting algorithm [Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. ], analyse the correlation between the on-chain/off-chain sample data, and describe additional experiments that empirically prove our findings and, finally, perform a comparison of our outputs with those from the Prophet [Taylor, S. J., & Letham, B. (2017). Forecasting at scale. PeerJ Preprints, 5, e3190v2. ] model. [ABSTRACT FROM AUTHOR]

This paper presents a Machine Learning and IoT-based intelligent medical system for the detection and monitoring of patient stress. This system is made up of a medical kit measuring the oxygen saturation, the heart rate and the galvanic skin response thanks to sensors attached at the top of the patient's hand which send the measured physiological values to the Firebase server. A voting classifier, combining five Machine Learning algorithms (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree and Random Forest) using holdout and K-fold cross-validation, was implemented on a Raspberry board installed in the doctor's office. The proposed system can make predictions with the Soft Voting classifier with an accuracy that reaches 78%. [ABSTRACT FROM AUTHOR]

In recent years, web marketing has thrived, and online coupon distribution has become a significant marketing measure that leads to increased sales. However, randomly distributing coupons risks lowering the profit ratio of companies. Therefore, it is important to estimate the effect of coupons and analyze the causal relationship between coupons and results. The potential purchase intention (PPI) of users is believed to influence the effect of coupons. For example, distributing coupons to users with a low PPI is likely to increase the gross profit of companies, whereas distributing coupons to users with a high PPI is likely to decrease the gross profit. Therefore, by analyzing the relationship between PPI and the effect of coupons, highly effective targeting can be conducted based on the PPI. In this paper, we propose an experimental design based on machine learning to analyze the effect of coupons, which varies depending on the PPI. We propose a method to predict users' PPI based on their purchase history data using machine learning and analyze the relationship between PPI and the effect of coupons. Finally, we demonstrate the effectiveness of the proposed framework by applying it to real-world data. [ABSTRACT FROM AUTHOR]

Remote sensing, particularly satellite-based, can play a valuable role in monitoring areas prone to geohazards. The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards. This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data, with a particular emphasis on: (1) detecting mining subsidence, where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar (InSAR) technology. The results showed that the Efficient Channel Attention (ECA) U-Net model performed better than the U-Net (baseline) model in terms of Mean Intersection over Union (MIoU) and Intersection over Union (IoU) indicators; (2) monitoring water conservancy and hydropower engineering. The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets (SBAS) InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side. The dam body also showed obvious deformation with a velocity exceeding 60 mm/a; (3) the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation. The overall outcome of these methods showed that the use of Artificial Intelligence (AI) techniques in combination with InSAR data leads to more efficient analysis and interpretation, resulting in improved accuracy and prompt identification of potential hazards; and (4) finally, this study also presents a method for detecting landslides in mountainous regions, using optical imagery. The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods, this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades. [ABSTRACT FROM AUTHOR]

Polymers, with various advantages including lightweight, low cost, flexibility and ease of processing, are popular choices for thermal management in flexible electronics, photovoltaics, fuel cells, etc. In this perspective, we review the recent progress of developing uniaxial thermally conductive polymers and three-dimensional polymers with high thermal conductivity, discuss new opportunities in applying machine learning methods to search for thermally conductive polymers, and finally point out current key challenges and future directions in simulations and experimental measurements. The useful insights in this perspective can guide the rational design of thermally conductive polymers for advanced thermal management. This paper delineates the advantages and recent progress of pristine polymers with high thermal conductivity and provides significant insights on future directions in the simulations and experimental measurements of thermally conductive pristine polymers. [ABSTRACT FROM AUTHOR]

The growing Internet of Things (IoT) landscape requires robust security; traditional rule-based systems are insufficient, driving the integration of machine learning (ML) for effective intrusion detection. This paper provides an inclusive overview of research efforts focused on harnessing ML methodologies to fortify intrusion detection within IoT. Tailored feature extraction techniques are pivotal for achieving high detection accuracy while minimizing false positives. The study employs the IoT23 dataset from Kaggle and incorporates four optimization algorithms – Particle Swarm Optimizer, Whale-Pearson optimization algorithm, Harris-Hawks Optimizer, and Support Vector Machine with Particle Swarm optimization algorithm (SVM-PSO) – for feature extraction and selection. A comparison with ML algorithms such as logistic regression, decision tree and naïve Bayes classifier highlights Harris-Hawks Optimizer as the most effective. Furthermore, ensemble methods, particularly the fusion of random forest with HHO optimization, yield an impressive accuracy of 99.97%, surpassing AdaBoost and XGBoost approaches. This paper underscores the application of diverse ensemble learning techniques to enhance intrusion detection precision and efficiency within the intricate IoT landscape, effectively tackling the challenges posed by its complex and ever-changing nature. [ABSTRACT FROM AUTHOR]

Piezoelectric semiconductors (PSs) have widespread applications in semiconductor devices due to the coexistence of piezoelectricity and semiconducting properties. It is very important to conduct a theoretical analysis of PS structures. However, the present of nonlinearity in the partial differential equations (PDEs) that describe those multi-field coupling mechanical behaviors of PSs poses a significant mathematical challenge when studying these PS structures. In this paper, we present a novel approach based on machine learning for solving multi-field coupling problems in PS structures. A physics-informed neural networks (PINNs) is constructed for predicting the multi-field coupling behaviors of PS rods with extensional deformation. By utilizing the proposed PINNs, we evaluate the multi-field coupling responses of a ZnO rod under static and dynamic axial forces. Numerical results demonstrate that the proposed PINNs exhibit high accuracy in solving both static and dynamic problems associated with PS structures. It provides an effective approach to predicting the nonlinear multi-field coupling phenomena in PS structures. [ABSTRACT FROM AUTHOR]

In software testing, the quality of the test suite plays a very important role for not only the effectiveness of the testing but also the quality assurance of software. Mutation testing is considered as the usable, automatic and very effective technique in detecting mistakes of the set of test cases such as missing test cases, redundant test cases··· However, when using the mutation testing technique in practice, the generation of a large number of mutants has led to very high computational costs. This raises the question of whether we can reliably and accurately predict this mutation score without running mutants or not. If we can do this, it will save a lot of time and effort but still ensure the effectiveness of mutation testing. In this paper, we propose the approach using machine learning to perform mutation score cross-prediction for software which are new and completely different from the software used to generate test data (mutants) in model training and testing. The experimental results have shown that our proposed approach has achieved the positive results and is highly feasible. Thus, we believe that the approach can be applied to significantly reduce the cost of mutation testing. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to utilizing open-source legal databases in academic education, especially in the fields of law and police investigations. Our framework provides a way to organize and analyze this data and extract reports that are associated with crime scenes, addressing the challenge of classifying unstructured legal documents by using text mining, natural language processing, and machine learning techniques. We developed a supervised machine learning model capable of accurately classifying court documents based on two classifiers: one identifies the documents containing crime scenes, and the other classifies them into five types of crimes. The experimental results were promising, as the random forest algorithm achieved an accuracy of 91.07% for the first classifier and support vector machines achieved an accuracy of 82.46% for the second classifier. What distinguishes our work is the creation of a crime dictionary that includes 70 crime tools and 151 related terms extracted from various forensic sources. It is considered relatively small, but it contributed to giving good classification results. The proposed crime dictionary can be generalized, developed, used in advanced searches, and integrated with police databases to improve crime scene analysis. Overall, the research highlights the use of court databases in police academic education and attempts to utilize them in a more effective manner. [ABSTRACT FROM AUTHOR]

Studies reviewed in this paper show anomaly for temperature pertaining to streamflow and rainfall showing different trends, especially in Ethiopia to support the research findings and interpretation. There are many hydrological models, including 54 physically distributed, lumped, and conceptual hydrological models, of which 28 have been used in Ethiopian river basins. The models include the most adaptable and commonly used SWAT model applicable from small areas up to large basins. It is indeed a challenge to use a single hydrological model as the data rely on consistency, limitation-free, and exactly fitted output. The overall performance of individual physically-based, conceptual, and machine learning (ML) models varied at different watersheds. Reasonably, ML performs very well, up to 0.99 for R² and NSE and up to 0.001 for PBIAS. Inopportunely, using a single hydrological model has its limitations; ensemble multi-individual models, coupling or hybridization of physical or conceptual models with machine learning, combining evolutionary optimization algorithms with ML, and also comparisons of multi-single hydrological models, and selecting the best one are recommended options. No single hydrological model is indispensable and can be termed as better than the other for any watershed. Somewhat, ML outperforms SWAT but cannot be considered an absolute substitute. The size of the watershed, the number of data used, and the ratio between calibrations year to validation year do not have a clear correlation with the performance, particularly for the SWAT model accounted for in this review. Optimization algorithms explore multiple options and choosing the right one is a tedious task before a final decision is taken. [ABSTRACT FROM AUTHOR]

To correctly evaluate the effects of treatments, conducting randomized controlled trials (RCTs) is a reasonable approach. However, because it is generally difficult to implement RCTs for all treatments, methods to estimate the treatment effects using observational data have been actively studied and used in various decision-making processes. Observational data accumulated in business activities and elsewhere contains the results of various previously implemented treatments, and correctly estimating the effects of any given treatment without separating the impacts of other treatments is challenging. Against this background, this paper proposes a method to estimate the effects of multiple treatments of various types while considering various causal relationships. Specifically, the proposal is a variational inference method that estimates the effect of multiple treatments using four latent factors estimated from observations, making assumptions that are independent of the type and number of treatments. The proposed method makes it possible to appropriately estimate the effects of measures even in situations with complex causal relationships. In addition, in situations where measures with continuous parameters are being implemented, it is possible to estimate the effects of measures that have not been implemented in the past by treating the content of the measures as a continuous variable. [ABSTRACT FROM AUTHOR]

The challenge of swift and reliable earthquake location prediction within earthquake early warning (EEW) systems underscore the need for innovative solutions. Existing methods provide predictions, yet there is a clear demand for enhanced accuracy and expediency in the process of resolving. The article intends in prediction of both magnitude of earthquakes and the affected areas in a specific region, focusing on California, United States, by leveraging historical data using neural networks (NNs). Neural networks outperform other algorithms such as XGBoost, linear regression, random forest, gradient boosting and support vector machine (SVM), in terms of high R² score and low score of MAE and also mean squared error (MSE). The derived results of R² score, MSE and MAE are 0.1607, 0.1615 and 0.2951, respectively. It seamlessly integrates geospatial visualization through Folium and GeoPandas. These tools enhance the predictive model's capabilities by generating dynamic maps enriched with markers, each representing the anticipated impact of earthquakes. The user interface facilitates interactive input, enabling users to input earthquake parameters for real-time predictions. The resultant map not only showcases the predicted impact zone but also provides valuable insights into the severity of seismic events. It stands as a testament to the synergy between machine learning and geospatial visualization, offering a holistic solution for earthquake prediction and geographical representation. [ABSTRACT FROM AUTHOR]

AbstractHigher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this paper we propose the (subgaussian) tensor mixed-membership blockmodel, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the identifiability of our model and propose a computationally efficient estimation procedure based on the higher-order orthogonal iteration algorithm (HOOI) for tensor SVD composed with a simplex corner-finding algorithm. We then demonstrate the consistency of our estimation procedure by providing a per-node error bound under subgaussian noise, which showcases the effect of higher-order structures on estimation accuracy. To prove our consistency result, we develop the ℓ2,∞ tensor perturbation bound for HOOI under independent, heteroskedastic, subgaussian noise that may be of independent interest. Our analysis uses a novel leave-one-out construction for the iterates, and our bounds depend only on spectral properties of the underlying low-rank tensor under nearly optimal signal-to-noise ratio conditions such that tensor SVD is computationally feasible. Finally, we apply our methodology to real and simulated data, demonstrating some effects not identifiable from the model with discrete community memberships. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, we study the transfer learning problem in functional classification, aiming to improve the classification accuracy of the target data by leveraging information from related source datasets. To facilitate transfer learning, we propose a novel transferability function tailored for classification problems, enabling a more accurate evaluation of the similarity between source and target dataset distributions. Interestingly, we find that a source dataset can offer more substantial benefits under certain conditions than another dataset with an identical distribution to the target dataset. This observation renders the commonly-used debiasing step in the parameter-based transfer learning algorithm unnecessary under some circumstances to the classification problem. In particular, we propose two adaptive transfer learning algorithms based on the functional Distance Weighted Discrimination (DWD) classifier for scenarios with and without prior knowledge regarding informative sources. Furthermore, we establish the upper bound on the excess risk of the proposed classifiers, providing the statistical gain via transfer learning mathematically provable. Simulation studies are conducted to thoroughly examine the finite-sample performance of the proposed algorithms. Finally, we implement the proposed method to Beijing air-quality data, and significantly improve the prediction of the PM 2.5 level of a target station by effectively incorporating information from source datasets. [ABSTRACT FROM AUTHOR]

With the progressive enhancement of social media, several people prefer posting their opinions on various social media instead of posting on radios, television or newspapers. The postings differ in dimensions and include various titles and comments. Nowadays, the formation of plagiarism is increasing tremendously which occurs by rewriting or repeating one’s work. There are many ways to detect plagiarism by browsing through the internet. The significant intention of this paper involves the detection of plagiarism in social media using four different phases, namely the data pre-processing phase, n-gram evaluation, similarity/distance computation analysis and the plagiarism detection phase. The pre-processing includes data cleaning processes, such as the removal of redundant data, upper case letters, noise, irrelevant punctuations and characterizing into a vector form. After pre-processing the data are fed for n-gram evaluation to develop a post- ing attribution system. Then finally, an ensemble support vector machine-based African vulture optimization (ESVM-AVO) approach is employed to detect plagiarism which signifies that the performance based on detection is enhanced and the execution time in obtaining a high rate of detection accuracy is very low. Finally, the performance evaluation and the comparative analysis are carried out to determine the performance of the proposed system. [ABSTRACT FROM AUTHOR]

The United Nations established the 17 Sustainable Development Goals (SDGs) in 2015 to address issues like gender equality, clean water, health, education, and hunger by 2030. Of the 17 SDGs, health and education have an outsized impact on countries’ socioeconomic development, so providing insights into progress on these two goals is crucial. Machine learning can help solve many real-world problems, including working towards the SDGs. This paper proposes using a metaheuristic ensemble of Cat Swarm Optimization algorithms with Feed Forward Extreme Learning Machines, called Sustainable Health And Educational Goal Development (SHEGD) Prediction, to effectively contribute to countries’ economic growth by achieving health and education SDGs through machine learning. The model is assessed using UN SDG datasets and performance metrics like accuracy, precision, recall, specificity, and F1-score. Comparisons to other machine learning models demonstrate this model’s superiority in designing a recommendation system for progressing towards the health and education SDGs. The proposed model outperforms the other approaches, proving its value for an SDG recommendation system design. [ABSTRACT FROM AUTHOR]

Telemedicine systems are gaining popularity due to their ability to provide remote medical services. These systems produce a lot of data, which may be used for a variety of purposes, including quality improvement, decision-making, and predictive analytics. Deep learning is an effective data mining method that may be applied to this data to bring out significant findings. Telemedicine systems, which allow patients to receive medical consultation and treatment remotely, generate vast amounts of data. Analyzing this data can provide valuable insights for improving patient care and optimizing the telemedicine system. Data mining techniques can be incredibly valuable for telemedicine systems, as they can help to identify patterns and insights in large amounts of patient data. Data mining techniques can assist telemedicine systems in making better decisions and offer better care to patients. In this paper, a novel framework for multiple disease prediction in telemedicine system using an effective deep learning algorithm was developed. The proposed multiple disease prediction system is composed of Long Short Term Memory (LSTM) unit. The experimental results revealed that the suggested disease prediction model exceeded the present models with an accuracy of 98.51%. [ABSTRACT FROM AUTHOR]

The novelty of this paper revolves around monitoring the condition of nitrogen-filled tires through the fusion of features and utilization of weightless neural networks. A tire pressure monitoring system (TPMS) plays a crucial role in ensuring vehicle safety and comfort. Reckless driving, poor road conditions, continual operation, higher road friction and excessive load are certain factors that can degrade the longevity of tires. Such conditions can result in instantaneous fault attacks in tires raising a concern for safety and comfort. To apply instantaneous and accurate fault diagnosis, the present study leverages machine learning techniques through the integration of an adaptive and robust algorithm, namely, the Wilkes, Stonham and Aleksander Recognition Device (WiSARD) classifier. The experiment uses three types of features namely, statistical, histogram and autoregressive moving average (ARMA) features. The J48 decision tree algorithm was used to pinpoint the key attributes crucial for classification. Following this, the identified attributes were segregated into training and testing datasets, facilitating the evaluation of the WiSARD classifier. Hyperparameter tuning was carried out to achieve optimal value for maximizing classification accuracy and minimizing computational time. Among the features considered, ARMA features delivered the best test set accuracy of about 96.18%. [ABSTRACT FROM AUTHOR]

Organizations have to establish strong security operations to protect their digital assets since cyberattacks are becoming more prevalent and sophisticated. Integrating threat intelligence into security operations is a fundamental strategy for enhancing an organization's security posture. However, the precision and dependability of the underlying machine learning classifiers employed for analysis determine how successful such platforms really are. In this paper, we leverage the UNSW-NB15 dataset to propose an integrated threat intelligence platform for security operations in organizations. In order to determine which machine learning classifier performs best, we run a variety of classifiers to the dataset, including Ensemble Learning, Stochastic Gradient Descent (SGD), Logistic Regression, and Ridge Classifier. Our findings demonstrate that the Ensemble Learning classifier beats the other classifiers, with accuracy, precision, recall, and F1 score of 97.02%, 98.34%, 99.02% and 98.17% respectively. This suggests that our proposed system is quite good at detecting potential threats and may offer insightful information for security operations in organizations. [ABSTRACT FROM AUTHOR]

This paper highlights an innovative approach to address the environmental challenges associated with waste management and construction materials. The study presents a new technique for utilising treated sewage sludge in the production of eco-friendly geopolymer mortar. Sewage sludge is a byproduct generated from wastewater treatment plants that poses significant environmental issues due to its disposal problems. Geopolymer mortar, on the other hand, is a sustainable construction material that can be produced by activating industrial waste materials, such as fly ash, using alkaline activators. By integrating machine learning algorithms and data visualisation techniques, this study optimises the mix parameters of geopolymer mortar while simultaneously reducing the negative impact of sewage sludge disposal. The experimental results show that sewage sludge ash can replace fly ash and fine aggregate in 1:3 geopolymer mortar by up to 30% and 20%, respectively, without any toxic or heavy metal residues. The compressive and flexural strength of the sludge-based mortar were comparable to ordinary geopolymer mortar, while water absorption and drying shrinkage were similar to control specimens. This research offers a unique solution to promote sustainability in waste management and construction industry by utilising waste materials, including fly ash and treated sewage sludge, for geopolymer production. [ABSTRACT FROM AUTHOR]

Natural streamflow data is required in many hydrological applications. However, many basins are located in data-scarce regions or are impacted by human construction and activities. In this paper, we explore three machine learning algorithms, namely artificial neural networks, random forest and light gradient boosting machine, to simultaneously estimate all the parameters of the coupled modèle du Génie Rural à 4 paramètres Journaliers (GR4J) and snow accounting routine called CemaNeige model. A database of 675 basins in the USA and Quebec is used to train and test ensembles. After using the estimated parameters in GR4J, the resulting naturalized streamflow series are compared with those obtained by the established drainage area ratio and spatial proximity transfer methods in 11 test basins. The results indicate that the machine learning algorithms outperform the drainage area ratio and spatial proximity transfer methods. Among machine learning algorithms, random forests obtain lower (better) continuous ranked probability scores than the other methods for 10 out of 11 test basins. [ABSTRACT FROM AUTHOR]

Accurate anticipation of the maize harvest date is important in the agricultural market, as it ensures the sustainability of food production in response to the increasing global demand for food. This paper proposes a predictive model to determine the optimal harvest time in maize plots using the Normalised Difference Vegetation Index (NDVI) and climatological data. These variables were oversampled and used to train various models, including Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Machine (XGBoost), CatBoost and Support Vector Machine (SVM). Bayesian optimisation has been used to find the best hyperparameters and Shapley values to identify the variables that exert the most significant influence on the prediction in each model instance. As a result of this approach, a model with an accuracy of 92.1% and an Area Under the Curve (AUC) of 0.935 was developed. The variables that determined these results were atmospheric pressure, mean temperature, precipitation, NDVI, and precipitation. [ABSTRACT FROM AUTHOR]

According to World Health Organization (WHO) data, cardiovascular diseases (CAD) continue to take the lives of more than 17.9 million people worldwide each year. Heart attacks are considered a fatal disease in this category, especially for older adults, which highlights the need to employ artificial intelligence to anticipate this disease. This research faces many challenges, starting with data quality and availability, where AI models require large and high-quality datasets for training. Elderly populations exhibit various health conditions, lifestyle factors, and genetic diversity. Creating AI models that can accurately generalize across such a diverse group can be challenging. Two datasets for CAD diseases were used for this study. Traditional machine learning (ML) techniques were used on these datasets, as well as a neural network method based on extreme learning machines (ELM), which provided varying percentages of accuracy, time, and average estimated error. The ELM algorithm outperformed all other algorithms by attaining the best accuracy, the shortest execution time, and the lowest percentage of average estimated error. Experimental results showed that the Extreme learning machine performed well with 200 hidden neurons, even with the proposed absence of parts of the dataset, with an accuracy of 97.57-99.06%. [ABSTRACT FROM AUTHOR]

The rapid expansion of technical infrastructure has brought about transformative changes in business operations. A notable consequence of this digital evolution is the proliferation of subscription-based services. With an increasing array of options for goods and services, customer churn has emerged as a significant challenge, posing a threat to businesses across sectors. The direct impact on earnings has prompted businesses to proactively develop tools for predicting potential client turnover. Identifying the underlying factors contributing to churn is crucial for implementing effective retention strategies. Our research makes a pivotal contribution by presenting a churn prediction model designed to assist businesses in identifying clients at risk of churn. The proposed model leverages machine learning classification techniques, with the customer data undergoing thorough pre-processing phases prior to model application. We systematically evaluated ten classification techniques, including Logistic Regression, Support Vector Classifier, Kernel SVM, KNN, Gaussian Naïve Bayes, Decision Tree Classifier, Random Forest, ADA Boost, XGBoost, and Gradient Boost. The assessment encompassed various evaluation metrics, such as ROC AUC Mean, ROC AUC STD, Accuracy Mean, Accuracy STD, Accuracy, Precision, Recall, F1 Score, and F2 Score. Employing 10-fold cross-validation and hyper parameter tuning through GridSearchCV and RandomizedSearchCV, we identified Random Forest as the most effective classifier, achieving an 85% Area Under the Curve (AUC) for optimal results. [ABSTRACT FROM AUTHOR]

\nImpact statementThe martensite start temperature (MS) plays a pivotal role in formulating heat treatment regimes for steel. This paper, through the compilation of experimental data from literature and the incorporation of expert knowledge to construct features, employs machine learning algorithms to predict the MS of steel. The study highlights that the ETR algorithm attains optimal prediction accuracy, and the inclusion of atomic features enhances the model’s performance. Feature selection is accomplished by evaluating linear and nonlinear relationships between data using the Pearson correlation coefficient (PCC), variance inflation factor (VIF), and maximum information coefficient (MIC). Subsequently, the performance of machine learning models on unknown data is compared to validate the model’s generalization ability. The introduction of SHAP values for model interpretability analysis unveils the influencing mechanisms between features and the target variable. Finally, utilizing a specific steel type as an illustration, the paper underscores the practical value of the model.This study innovatively integrates experimental data, expert knowledge, and ETR algorithm for accurate MS prediction in steel, enhancing model performance with systematic feature selection and interpretability analysis, demonstrating practical utility. [ABSTRACT FROM AUTHOR]

Introduction: Expanding the use of surface electromyography-biofeedback (EMG-BF) devices in different therapeutic settings highlights the gradually evolving role of visualizing muscle activity in the rehabilitation process. This review evaluates their concepts, uses, and trends, combining evidence-based research. Areas covered: This review dissects the anatomy of EMG-BF systems, emphasizing their transformative integration with machine-learning (ML) and deep-learning (DL) paradigms. Advances such as the application of sophisticated DL architectures for high-density EMG data interpretation, optimization techniques for heightened DL model performance, and the fusion of EMG with electroencephalogram (EEG) signals have been spotlighted for enhancing biomechanical analyses in rehabilitation. The literature survey also categorizes EMG-BF devices based on functionality and clinical usage, supported by insights from commercial sectors. Expert opinion: The current landscape of EMG-BF is rapidly evolving, chiefly propelled by innovations in artificial intelligence (AI). The incorporation of ML and DL into EMG-BF systems augments their accuracy, reliability, and scope, marking a leap in patient care. Despite challenges in model interpretability and signal noise, ongoing research promises to address these complexities, refining biofeedback modalities. The integration of AI not only predicts patient-specific recovery timelines but also tailors therapeutic interventions, heralding a new era of personalized medicine in rehabilitation and emotional detection. [ABSTRACT FROM AUTHOR]

\nImpact StatementLignocellulosic materials have inherent complexities and natural nanoarchitectures, such as various chemical constituents in wood cell walls, structural factors such as fillers, surface properties, and variations in production. Recently, the development of lignocellulosic filler-reinforced polymer composites has attracted increasing attention due to their potential in various industries, which are recognized for environmental sustainability and impressive mechanical properties. The growing demand for these composites comes with increased complexity regarding their specifications. Conventional trial-and-error methods to achieve desired properties are time-intensive and costly, posing challenges to efficient production. Addressing these issues, our research employs a data-driven approach to streamline the development of lignocellulosic composites. In this study, we developed a machine learning (ML)-assisted prediction model for the impact energy of the lignocellulosic filler-reinforced polypropylene (PP) composites. Firstly, we focused on the influence of natural supramolecular structures in biomass fillers, where the Fourier transform infrared spectra and the specific surface area are used, on the mechanical properties of the PP composites. Subsequently, the effectiveness of the ML model was verified by selecting and preparing promising composites. This model demonstrated sufficient accuracy for predicting the impact energy of the PP composites. In essence, this approach streamlines selecting wood species, saving valuable time.This paper introduces a data-driven method to efficiently design lignocellulosic polymer composites with high-impact energy, optimizing components and surface areas using infrared spectroscopic data. [ABSTRACT FROM AUTHOR]

Assessment of crop nitrogen status is essential for efficient crop growth management. Existing nitrogen measurements are accurate but destructive, laborious, and time-consuming. Therefore, the soil plant analysis development (SPAD) meter approach is commonly used to address these challenges along with locationspecific measurements. The study aims to develop a robust machine learning-based model for predicting rice crop SPAD values using spectral data and to generate spatial maps of SPAD values and nitrogen content. The SPAD meter data, UAV-based multispectral images, and spectroradiometer-based data were collected during Rabi 2021/22 and 2022/23 seasons. The red and rededge bands, Normalized Difference Vegetation Index, and Normalized Pigment Chlorophyll Index correlated well with SPAD values. The random forest regressor model performed well with UAV-based data compared to support vector regression and partial least square regression and achieved good accuracy with the ground truth spectroradiometer data. This generalized model demonstrates adaptability in precisely assessing crop nitrogen status. [ABSTRACT FROM AUTHOR]

Extracting building from remote sensing images is crucial, but the extracted outlines still face issues such as point redundancy and lack of right-angle features, relying on further regularization. This study proposed an integrated regularization method to combine the strengths of multiple algorithms. 22 metrics were calculated to describe the geometric characteristic of outlines, followed by principal component analysis for selecting key metrics. Further, a supervised learning model was constructed to analyze these key metrics and determine the most suitable algorithm among three candidates-rectangle transformation, recursive regression, and feature edge reconstruction-for regularizing each building. Experimental results demonstrated that our method can adaptively select the appropriate algorithm based on the metrics, achieving regularization results superior to those obtained by using algorithms independently. Compared with two existing geometric correctionbased methods, our method excels in preserving the orientation, area, and shape. Our method also has advantages over learningbased methods in maintaining the orthogonality. [ABSTRACT FROM AUTHOR]

Traditional landslide susceptibility assessment methods often rely on single models, which can be biased and less accurate. In this article, we introduce a two-tiered strategy to enhance landslide susceptibility predictions. Initially, we employ an ensemble stacking technique that combines the strengths of three machine learning classifiers. This combination leverages the support vector classifier (SVC) as the key meta-classifier to optimize and refine predictions. Subsequently, we integrate the extreme gradient boosting (XGB), random forest (RF) and gradient boosting decision tree (GBDT) models with SVC to create hybrid approaches. In this study, we evaluate and compare the effectiveness of six machine learning algorithms for predicting landslide susceptibility in Anhua County, Hunan Province, China. The results demonstrated that the stacking ensemble model outperforms traditional models. The XBGþSVC model achieves the highest AUC value (0.9468), which is followed by the GBDTþSVC (0.9316), RFþSVC (0.9162), XGB (0.9393), GBDT (0.9009), and RF (0.8693). These findings indicate that stacking machine learning approaches hold promise for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

After the earthquake in Wenchuan in 2008, landslides have been happening frequently, causing a buildup of sediment in slopes and gullies. This creates a potential for mudslides and flash floods. It is important to create a model to assess the likelihood of landslides in Wenchuan County for disaster prevention. This study focuses on Wenchuan County, using 6180 co-seismic landslide sites as data. The relevant survey data is combined, and slope, normalized difference vegetation Index (NDVI), peak seismic acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance from the road, distance from the fault, distance from the water system, relief intensity, aspect, curvature, land use type, and lithologic characters are chosen. The significance of the co-seismic landslide key influence factors is discussed. The study area's disaster susceptibility was assessed using the informative hierarchical analysis method model (I-AHP) and fully connected neural network (FCNN) model; the evaluation's findings mostly agreed with the survey's actual findings. The study's findings demonstrated that: (1) the FCNN model's AUC value was 0.910, while the I-AHP model's AUC value was 0.768, and the ROC curve and AUC value analysis indicated that the FCNN model outperformed the I-AHP model. (2) The three most significant influencing factors are lithology, topographic relief, and distance from roads; they account for 29.5%, 11%, and 10% of the total, respectively. Thus, landslide hazard research should be done in the high terrain undulating areas surrounding the road, and rock monitoring should be intensified in post-earthquake landslide-prone areas like Wenchuan County. (3) Given that Yingxiu Town is the epicentre of the region and that landslide disasters are highly likely to occur there, technical support for landslide disaster prediction, forecasting, safe prevention, and control in the area needs to be strengthened. [ABSTRACT FROM AUTHOR]

This research investigates the suitability of PRISMA and Sentinel-2 satellite imagery for retrieving topsoil properties such as Organic Matter (OM), Nitrogen (N), Phosphorus (P), Potassium (K), and pH in croplands using different Machine Learning (ML) algorithms and signal pre-treatments. Ninety-five soil samples were collected in Quzhou County, Northeast China. Satellite images captured soil reflectance data when bare soil was visible. For PRISMA data, a Linear Mixture Model (LMM) was used to separate soil and Photosynthetic Vegetation (PV) endmembers, excluding Non-Photosynthetic Vegetation (NPV) using Band Depth values at the 2100 nm absorption peak of cellulose. Sentinel-2 bare soil reflectance spectra were obtained using thresholds based on NDVI and NBR2 indices. Results showed PRISMA data provided slightly better accuracy in retrieving topsoil nutrients than Sentinel-2. While no optimal predictive algorithm was best, absorbance data proved more effective than reflectance. PRISMA results demonstrated potential for predicting soil nutrients in real scenarios. [ABSTRACT FROM AUTHOR]

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays. [ABSTRACT FROM AUTHOR]

Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is unconventional to many students in a second or third course in statistics or data science. As a result, this topic is often not as intuitive to students as some of the more traditional statistical modeling tools. In order to facilitate students' mastery of the topic and promote active learning, we developed some in-class activities and their accompanying Shiny applications for teaching support vector classifiers. The designed course materials aim at engaging students through group work and solidifying students' understanding of the algorithm via hands-on explorations. The Shiny applications offer interactive demonstration of the changes of the components of a support vector classifier when altering its determining parameters. With the goal of benefiting the broader statistics and data science education community, we have made the developed Shiny applications publicly available. In addition, a detailed in-class activity worksheet and a real data example are also provided in the online . [ABSTRACT FROM AUTHOR]

While social media can serve as public discussion forums of great benefit to democratic debate, discourse propagated through them can also stoke political polarization and partisanship. A particularly dramatic example is the January 6, 2021 incident in Washington D.C., when a group of protesters besieged the US Capitol, resulting in several deaths. The public reacted by posting messages on social media, discussing the actions of the participants. Aiming to understand their perspectives under the broad concept of unhealthy online conversation (i.e. bad faith argumentation, overly hostile or destructive discourse, or other behaviours that discourage engagement), we sample 1,300,000 Twitter posts taken from the #Election2020 dataset dating from January 2021. Using a fine-tuned XLNet model trained on the Unhealthy Comment Corpus (UCC) dataset, we label these texts as healthy or unhealthy, furthermore using a taxonomy of 7 unhealthy attributes. Using the NRCLex sentiment analysis lexicon, we also detect the emotional patterns associated with each attribute. We observe that these conversations contain accusatory language aimed at the 'other side', limiting engagement by defining others in terms they do not themselves use or identify with. We find evidence of three attribute clusters, in addition to sarcasm, a divergent attribute that we argue should be researched separately. We find that emotions identified from the text do not correlate with the attributes, the two approaches revealing complementary characteristics of online discourse. Using latent Dirichlet allocation (LDA), we identify topics discussed within the attribute-sentiment pairs, linking them to each other using similarity measures. The results we present aim to help social media stakeholders, government regulators, and the general public better understand the contents and the emotional profile of the debates arising on social media platforms, especially as they relate to the political realm. [ABSTRACT FROM AUTHOR]

People inherently assess landscapes by creating spontaneous aesthetic liking judgments based on the surrounding stimuli. To understand these judgements objectively, use may be made of the fluency theory of aesthetic pleasure (the psychological processes through which people experience beauty). This study aims to predict people's visual aesthetic preferences based on fluency theory and to correlate these preferences with landscape types and features. An ordinary least squares (OLS) regression model was developed to predict visual aesthetic liking, using image statistics as explanatory variables. We determined types of landscape using Landscape Character Assessment (LCA) and applied viewshed analyses distinguishing between near, medium, and far zones. We identified landscape features by content analysis making use of machine learning-based image recognition supplied by Google Cloud Vision API. The results show that vegetation and geological forms were the most significant features for people's visual aesthetic liking, followed by waterscapes and built structures/human settlements. The viewshed analyses indicated that 'medium-altitude, low-gradient artificial areas' were visible in photographs with high aesthetic visual liking in all zones (i.e., at all distances). When the photographs showing this type of landscape are examined, the artificial areas in the photographs turn out to consist mostly of historical buildings or remains. This finding suggests that historical sites are not just important for their cultural value, but for their visual aesthetic value as well. [ABSTRACT FROM AUTHOR]

Synchrotron X-ray imaging has been utilised to detect the dynamic behaviour of molten pools during the metal additive manufacturing (AM) process, where a substantial amount of imaging data is generated. Here, we develop an efficient and robust deep learning model, AM-SegNet, for segmenting and quantifying high-resolution X-ray images and prepare a large-scale database consisting of over 10,000 pixel-labelled images for model training and testing. AMSegNet incorporates a lightweight convolution block and a customised attention mechanism, capable of performing semantic segmentation with high accuracy (~96%) and processing speed (< 4 ms per frame). The segmentation results can be used for quantification and multi-modal correlation analysis of critical features (e.g. keyholes and pores). Additionally, the application of AM-SegNet to other advanced manufacturing processes is demonstrated. The proposed method will enable end-users in the manufacturing and imaging domains to accelerate data processing from collection to analytics, and provide insights into the processes' governing physics. [ABSTRACT FROM AUTHOR]

The compressive strength of concrete is a critical parameter that affects the safety, durability, and performance of structures. This is a key factor in the design, construction quality control, and long-term functionality of concrete elements in various engineering applications. This is a fundamental indicator of the ability of the concrete to withstand axial loads or pressures. Understanding the compressive strength is essential for ensuring the safety of structures. This helps engineers design concrete elements with sufficient load-bearing capacity to withstand the expected loads and maintain structural stability. The objective of this study is to assess the effectiveness of linear and non-linear machine learning algorithms in accurately predicting the compressive strength of concrete based on varying quantities of constituent components. To address outliers in the data, we utilized an iterative implicit technique during the data preprocessing stage. Our implementation included Linear models such as Linear regression and polynomial regression, Non-linear models such as Decision Tree and Random Forest, and Boosting models such as Gradient Boosting and Extreme Gradient Boosting. The accuracy scores and overfitting of the models were compared. The models were evaluated using the R² scores for both the training and test sets. Among the three categories of models tested, boosting models demonstrated higher accuracy than the other models, while also exhibiting limited overfitting. [ABSTRACT FROM AUTHOR]

In this article, we propose UPDEMIN, a novel approach for predicting UPcoming patient situation, DEcision Making and INtervention in wireless body sensor network. Inspired by the National Early Warning Score (NEWS) system, a new representation and usage of all the vital signs scores to create a diagnostic label (DL) representing the overall health situation. The formulation of the DL does not help only in accurate prediction of the upcoming health situation but provides a decoding mechanism to identify the reasons for deterioration and which vital signs are driving it. Simulations show high accuracy in predicting several upcoming situations ahead of time with 90% accuracy. Compared to other methods, UPDEMIN shows superior performance with less historical data or a smaller database, exceeding 91% prediction accuracy versus 66% for the closest method. The performance of the classification model has shown perfect precision and recall values, as demonstrated by the F1-score, which recorded 0.92. Moreover, the predicted situation can be decoded easily to identify which vital signs cause criticality, allowing the medical team to make the appropriate decision and prepare for the right intervention. The capability to decode the predicted health situation is unique and expected to have an insightful return on preventative intervention decisions. [ABSTRACT FROM AUTHOR]

Demand forecasting, a crucial aspect of anticipating future customer needs, involves using historical data to predict trends. With the rise of artificial intelligence (AI), companies are increasingly turning to machine learning algorithms to enhance accuracy in forecasting compared to traditional methods. This article delves into the application of machine learning algorithms in demand forecasting, specifically within the realm of supply chain management, addressing both long-term (4-5 years) and short-term (3-4 months) scenarios. The primary focus is on improving prediction accuracy by employing feature selection algorithms and various machine learning and deep learning approaches. Utilizing diverse algorithms, such as time series, traditional machine learning, and advanced deep learning techniques, the study aims to forecast demand for different timeframes. Evaluation metrics like Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) are employed to assess the effectiveness of each model. The primary goal is to pinpoint the most effective algorithm tailored to a specific dataset. This empowers companies to make well-informed decisions and enhance their supply chain operations by leveraging precise demand forecasts. The results of this study hold the potential to empower decision-makers and practitioners by enhancing their forecasting capabilities. By integrating both forecast periods a more comprehensive and robust supply chain strategy is ensured. [ABSTRACT FROM AUTHOR]

The proposed research aims to predict flood events in Amo Chhu river of Bhutan using machine learning and Internet of Things. The Amo Chhu River has been posing threat to the residents along river Amo Chhu, and this research seeks to improve flood forecasting to mitigate the impact of natural disasters on vulnerable communities. The research utilizes three machine learning models, including Random Forest, Gradient boosting, and Support Vector Classifier, in combination with IoT devices. The data required to train the machine learning models was obtained from National Centre for Hydrology and Meteorology (NCHM) ranged from 1996 to 2022. The current work evaluates the efficiency of these models in predicting flood events in Amo Chhu River. The proposed work found that the Gradient Boosting Classifier model delivered outstanding performance, achieving an accuracy of 99.8%, followed by Support Vector Classifier model with an accuracy of 98.3%. The result from Random Forest model showed promising outcome, with accuracy of 97.9%. The inclusion of real-time data from IoT devices significantly improved the effectiveness of the forecasting system thus indicating potential of machine learning models and IoT devices in improving flood forecasting and mitigating the impact of natural disasters on vulnerable communities. [ABSTRACT FROM AUTHOR]

Trichoderma Reesei is a mesophilic and filamentous fungus. It is an anamorph of the fungus Hypocrea jecorina, in addition, T. reesei can secrete large amounts of cellulolytic enzymes and form dextrose PDA (potato dextrose agar) and potato injection. After the preparation of fungi, it is added to the cracked samples. The experimental samples were 150 mm³ cubic compression and 70 mm x 30 mm x 15 mm cracks on the surface of each cube. Different fungi water extracts were used with 0, 50.5, 6.37, and 8.42 liters of water per ml. The results show that the addition of 8.42 (ml) of the mushroom extract with one liter of water has the maximum compressive strength with more than 18.99 MPa for 28 days, 16.7 for 14 days, and 14.5 for 7 days. In this study, linear regression, lasso regression, and rigid regression have been used to predict compressive strength, also the cooperation between mushroom juice per milliliter and compressive strength has been predicted. To find the accuracy, Correlation Coefficient (R²), Mean Absolute Errors (MAE), and Root Mean Square Error have been used. The results of machine learning show that the results of linear regression and rigid regression R² were more than 0.98. In addition, the relationship compressive strength prediction results showed that R² for fungi broth with one liter of water was 5.05 mL was more than 0.98. Finally, this study shows that the fungus Trichoderma reesei is an effective agent for curing concrete and improving the compressive strength of concrete. [ABSTRACT FROM AUTHOR]

Plant diseases, mainly caused by bacteria and fungi, affect crop yield and quality. Detecting disease symptoms at an early stage and promptly is a significant obstacle in safeguarding crops. In developing nations, experts and agronomists commonly opt for visual identification of diseases on vast farms, which incurs both time and monetary expenses. Scientists have suggested diverse deep neural network architectures for recognizing plant ailments. Nevertheless, deep learning algorithms necessitate a vast amount of parameters, which extends the training duration but yields commendable precision. While deep learning and Densenet are widely used in pesticide recommendations. Researchers have suggested diverse deep-learning architectures for detecting agricultural ailments and recommend appropriate pesticides. Test images were diagnosed using an automated Densenet model and the results were verified by plant pathologists. An accuracy of over 92% was achieved in identifying the disease. Our solution is an innovative, scalable and accessible tool for disease management of various crops that can be implemented as a cloud service for farmers and professionals involved sustainable agricultural production. [ABSTRACT FROM AUTHOR]

This study aims to set up a data-driven framework for optimization and performance prediction of a nanolubricant. Isoviscous approximation was applied to simplify analytical results. FDM methods were used to generate 1204 data points. A multilayer perceptron (MLP) was trained using this large dataset to achieve high testing accuracy (R = 1). The least mean squared error was found at 987 epochs for 23 hidden layer neurons. Multiple statistical tools were used to analyze the results. The error values in the load prediction (up to 7.34%) were observed to be higher than the friction coefficient (up to 0.041%) and side leakage (up to 0.05%). The JAYA algorithm, a parameter-independent machine learning algorithm was used for zone-specific optimization with the zones being low load region (eccentricity ratio: 0.1 to 0.3), medium load region (eccentricity ratio: 0.3-0.6), high load region (eccentricity ratio: 0.6-0.95) and whole range. The optimized combinations for low, medium, high and whole range of operation gave load capacity of 91 N, 357 N, 1328N and 124 N, respectively. The optimized results highlight the tendency to minimize the eccentricity ratio and maximize volume fraction and aggregate particle size ratio. [ABSTRACT FROM AUTHOR]

Handwritten character recognition (HCR) is a critical component of diverse applications, especially in multilingual regions such as India. Despite technological advancements, recognizing handwritten characters remains challenging because of variations in writing style, shape and linguistic nuances. This study presents an efficient HCR system tailored for Indian languages, such as Tamil and Telugu scripts, to address these challenges. The proposed nearest quad-tree (NQT) classifier enhances recognition accuracy while ensuring computational efficiency. This novel approach, a light-weight design, combines local feature-level decisions with a global decision-making process, thereby making it suitable for devices with limited computational resources. Key contributions include the emphasis of the NQT classifier on efficiency and lightweight design, applicability to regional languages and usability in resource-constrained settings. This study utilized three datasets from the Tamil, Devanagari and Telugu languages to demonstrate the exceptional performance of the NQT classifier in character recognition through the minimization of misclassification errors. The classifier exhibited consistently low misclassification error rates across a diverse range of characters, outperforming existing deep learning models. Notably, the NQT classifier achieved an overall accuracy, precision, recall and F1-measure of 96%, 95%, 97% and 96%, respectively, across the three datasets, surpassing the performance of popular transfer learning models. Furthermore, a comprehensive computational efficiency analysis highlighted the superior efficiency of the NQT classifier in terms of training time, CPU and GPU utilization and memory requirements, underscoring its potential for practical applications in resource-constrained environments. [ABSTRACT FROM AUTHOR]

The liver is an important and multifunctional human organ. Early and accurate diagnosis of a liver tumor can save lives. Computed Tomography (CT) images provide comprehensive information for liver tumor diagnosis using feature extraction techniques. These extracted features help classify liver tumors using Machine Learning (ML) and Deep Learning (DL) methods. For this research, twelve 1hundred CT images were acquired from the Radiology Department of Nishter Medical University & Hospital. The noise was removed using Gabor Filter after converting CT images into grayscale. Image quality was enhanced by adopting Histogram Equalization (HE), and finally, the Image's edges and boundaries were improved using a smoothening and sharpening algorithm. Preprocessed images were forwarded to extract six features: Histogram, Run-length, Co-occurrence, Autogressive, Gradient, and Wavelet Transform. A major focus of this research is to evaluate that ML methods produced good accuracy using already extracted features while DL Algorithms could not produce better results. Firstly, ML methods such as Decision Tree (DT), Random Forest (RF), Boost, and Support Vector Machine (SVM) are deployed using an already extracted feature list containing all six features. It was observed that DT, RF, Boost, and SVM produced 96.5%, 99.6%, 99.7%, and 98.0% classification accuracy. After that, DL Algorithms such as Neural Networks (NN), Long-Short Term Memory (LSTM), Bi-Directional Long Short Term Memory (Bi-LSTM), and Convolutional Neural Networks (CNN) were deployed. The results showed that NN, LSTM, Bi-LSTM, and CNN produced 50.0%, 53.0%, 54.0%, and 54.0% accuracy respectively. To validate the major focus of this research, finally, Pre-trained DL Algorithms such as Residual Network 50 (Resnet50), Visual Geometry Group 16 (VGG16) and LSTM + CNN were deployed. Results showed that Resnet50, VGG16, and LSTM + CNN attained 78.0%, 88.0%, and 97.0% accuracy respectively. Hence, ML methods performed better using already extracted features, while DL Algorithms could not produce promising results on these extracted features. [ABSTRACT FROM AUTHOR]

Before using concrete for a particular purpose, its strength must be determined because its physical properties vary depending on the type of supplementary cementitious material (SCMs). Initially, researchers anticipated it with statistical processes; more recently, they started implementing deep learning (DL) and machine learning (ML) models. This research employed ensemble machine learning (EML) approaches to predict the compressive strength (CS) of concrete developed for ground-granulated blast furnace slag (GGBFS) along with Alccofine 1203 (AF). Ensemble regressions of Random Forest Regression (RFR) and AdaBoost Regression (ABR) were considered for model prediction using the Jupyter Notebook. The models were created based on the outcome of the compressive strength under the 90 experimental conditions. Moreover, a comparative study was performed between the RFR and ABR to benchmark the proposed model against a given combination of features and CS. The experimental conditions were optimized using a genetic algorithm (GA) after the model was created by ANN. With the two EML algorithms, the smallest errors, along with the largest coefficient of determination, were observed in the RFR algorithm. It was recognized that the optimized value of CS is 48.606 MPa, and their optimized process elements such as cement, alccofine, GGBS, water, and days were 261.506 kg/m³, 0.055 kg/m³, 52.468 kg/m³, 141.007 L, and 27.999 days, respectively. [ABSTRACT FROM AUTHOR]

Decision-support methods are crucial for analyzing complex alternatives and criteria in today's data-driven world. This Systematic Literature Review (SLR) explores and synthesizes knowledge about decision support methodologies that integrate Multicriteria Decision Making (MCDM) and Principal Component Analysis (PCA), an unsupervised Machine Learning (ML) technique. Both techniques optimize complex decisions by combining multiple criteria and dimensional data analysis. Focusing on performance evaluations, criterion weighting, and validation testing, this review identifies significant gaps in existing methodologies. These include the lack of consideration for non-beneficial criteria in PCA, insufficient validation tests in over half of the studies, and the non-use of communalities (the contribution of each criterion to the main factors) in decision support approaches. Additionally, this SLR offers a comprehensive quantitative overview, analyzing data from the Scopus, IEEE, and Web of Science databases and identifying 16 relevant studies. Furthermore, the scarcity of systematic reviews integrating MCDM and PCA techniques impedes evidence-based decision- making practices and theoretical evolution. This is particularly crucial as ML and data analysis advance rapidly, requiring models that reflect technological innovations. This article addresses this gap in the literature by providing an analysis of decision support methods and guiding further improvement in this field. [ABSTRACT FROM AUTHOR]