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Featured Application: This research presents a practical application for efficiently evaluating the condition of asbestos–cement (AC) roofs in regions with limited regulatory frameworks. By leveraging machine learning and remote sensing techniques, the developed methodology can be used to prioritize roof replacements, mitigating health risks associated with asbestos exposure. The tool can assist policymakers and environmental agencies in developing effective strategies for asbestos management and disposal. This study introduces an innovative approach to evaluate the condition of asbestos–cement (AC) roofs by integrating field data with five distinct supervised learning models: decision trees, KNN, logistic regression, support vector machine, and random forest. A novel methodology for assessing the importance of 380 reflectance bands was employed, offering fresh insights into the key indicators of AC roof deterioration. The research systematically organized and prioritized reflectance bands based on their information gain, optimizing both the selection of relevant bands and the performance of the models in differentiating between low and high intervention priority (LIP and HIP) roofs. The decision tree model, when applied to the top 10 most relevant bands, achieved the highest cross-validation accuracy of 76.047%, making it the most effective tool for identifying AC roof conditions. Additionally, the random forest model demonstrated strong performance across various band groups, further validating its utility. Utilizing the open-source software Weka (version 3.8.6), this study adeptly executed relevance evaluation and model implementation, providing a practical and scalable solution for material characterization, especially in regions where resources for spectral and hyperspectral image analysis are limited. The findings of this study offer valuable tools for government and environmental authorities, particularly in developing countries, where efficient and cost-effective AC roof assessment is crucial for public health and safety. The methodology is adaptable to different urban environments and climatic conditions, supporting global efforts in asbestos management, especially in countries where asbestos regulations are newly implemented. Organized within the CRISP-DM framework, this paper details the methodological phases, presents compelling results on reflectance band relevance, evaluates machine learning models, and concludes with prospects for future research aimed at enhancing asbestos detection and removal strategies. [ABSTRACT FROM AUTHOR]

Equipment downtime resulting from maintenance in various sectors around the globe has become a major concern. The effectiveness of conventional reactive maintenance methods in addressing interruptions and enhancing operational efficiency has become inadequate. Therefore, acknowledging the constraints associated with reactive maintenance and the growing need for proactive approaches to proactively detect possible breakdowns is necessary. The need for optimisation of asset management and reduction of costly downtime emerges from the demand for industries. The work highlights the use of Internet of Things (IoT)-enabled Predictive Maintenance (PdM) as a revolutionary strategy across many sectors. This article presents a picture of a future in which the use of IoT technology and sophisticated analytics will enable the prediction and proactive mitigation of probable equipment failures. This literature study has great importance as it thoroughly explores the complex steps and techniques necessary for the development and implementation of efficient PdM solutions. The study offers useful insights into the optimisation of maintenance methods and the enhancement of operational efficiency by analysing current information and approaches. The article outlines essential stages in the application of PdM, encompassing underlying design factors, data preparation, feature selection, and decision modelling. Additionally, the study discusses a range of ML models and methodologies for monitoring conditions. In order to enhance maintenance plans, it is necessary to prioritise ongoing study and improvement in the field of PdM. The potential for boosting PdM skills and guaranteeing the competitiveness of companies in the global economy is significant through the incorporation of IoT, Artificial Intelligence (AI), and advanced analytics. [ABSTRACT FROM AUTHOR]

This study introduces an innovative approach to evaluate the condition of asbestos–cement (AC) roofs by integrating field data with five distinct supervised learning models: decision trees, KNN, logistic regression, support vector machine, and random forest. A novel methodology for assessing the importance of 380 reflectance bands was employed, offering fresh insights into the key indicators of AC roof deterioration. The research systematically organized and prioritized reflectance bands based on their information gain, optimizing both the selection of relevant bands and the performance of the models in differentiating between low and high intervention priority (LIP and HIP) roofs. The decision tree model, when applied to the top 10 most relevant bands, achieved the highest cross-validation accuracy of 76.047%, making it the most effective tool for identifying AC roof conditions. Additionally, the random forest model demonstrated strong performance across various band groups, further validating its utility. Utilizing the open-source software Weka (version 3.8.6), this study adeptly executed relevance evaluation and model implementation, providing a practical and scalable solution for material characterization, especially in regions where resources for spectral and hyperspectral image analysis are limited. The findings of this study offer valuable tools for government and environmental authorities, particularly in developing countries, where efficient and cost-effective AC roof assessment is crucial for public health and safety. The methodology is adaptable to different urban environments and climatic conditions, supporting global efforts in asbestos management, especially in countries where asbestos regulations are newly implemented. Organized within the CRISP-DM framework, this paper details the methodological phases, presents compelling results on reflectance band relevance, evaluates machine learning models, and concludes with prospects for future research aimed at enhancing asbestos detection and removal strategies.

Software-Defined Networking (SDN) is susceptible to security threats despite all the network programmability and flexibility offered, and hence SDN must be safeguarded.This work proposes an Intelligent Intrusion Detection System for Software-Defined Networks (SD-IIDS) that creates two equally competent ensemble Machine Learning (ML) classification models for detecting Distributed Denial of Service (DDoS) attacks in SDN. The developed ensemble models act as binary and multi-class classification algorithms. The models are Support Vector Classifier bagged with Random Forest (SVC-RF), and Random Forest bagged with Logistic Regression (RF-LR). The multi-class SVC-RF and RF-LR detect the the DDoS attack types with 98.83% and 99.54% accuracy and minimal False Alarm Rate (FAR) of 0.0189 and 0.012, respectively. The binary SVC-RF and RF-LR algorithms classify the network traffic into malicious and legitimate classes, with 99.42% and 99.79% accuracy and a nominal FAR of 0.0005 and 0.002, respectively. This work's core innovation is choosing the champion model among the two ensemble ML models based on its classification performance metrics and complexity analysis. The other major contribution of the work is botnet detection leveraging data mining techniques. The multi-class RF-LR ensemble outperformed multi-class SVC-RF with 99.45% precision and 99.46% sensitivity. The optimal performance metrics imply that the proposed ensemble models have greater efficacy than the individual ML models. This work paves the way for future research to detect the most potent volume-based and protocol-based DDoS attacks in SDN. [ABSTRACT FROM AUTHOR]

Equipment downtime resulting from maintenance in various sectors around the globe has become a major concern. The effectiveness of conventional reactive maintenance methods in addressing interruptions and enhancing operational efficiency has become inadequate. Therefore, acknowledging the constraints associated with reactive maintenance and the growing need for proactive approaches to proactively detect possible breakdowns is necessary. The need for optimisation of asset management and reduction of costly downtime emerges from the demand for industries. The work highlights the use of Internet of Things (IoT)-enabled Predictive Maintenance (PdM) as a revolutionary strategy across many sectors. This article presents a picture of a future in which the use of IoT technology and sophisticated analytics will enable the prediction and proactive mitigation of probable equipment failures. This literature study has great importance as it thoroughly explores the complex steps and techniques necessary for the development and implementation of efficient PdM solutions. The study offers useful insights into the optimisation of maintenance methods and the enhancement of operational efficiency by analysing current information and approaches. The article outlines essential stages in the application of PdM, encompassing underlying design factors, data preparation, feature selection, and decision modelling. Additionally, the study discusses a range of ML models and methodologies for monitoring conditions. In order to enhance maintenance plans, it is necessary to prioritise ongoing study and improvement in the field of PdM. The potential for boosting PdM skills and guaranteeing the competitiveness of companies in the global economy is significant through the incorporation of IoT, Artificial Intelligence (AI), and advanced analytics.

Survey research aims to collect robust and reliable data from respondents. However, despite researchers' efforts in designing questionnaires, survey instruments may be imperfect, and question structure not as clear as could be, thus creating a burden for respondents. If it were possible to detect such problems, this knowledge could be used to predict problems in a questionnaire during pretesting, inform real-time interventions through responsive questionnaire design, or to indicate and correct measurement error after the fact. Previous research has used paradata, specifically response times, to detect difficulties and help improve user experience and data quality. Today, richer data sources are available, for example, movements respondents make with their mouse, as an additional detailed indicator for the respondent–survey interaction. This article uses machine learning techniques to explore the predictive value of mouse-tracking data regarding a question's difficulty. We use data from a survey on respondents' employment history and demographic information, in which we experimentally manipulate the difficulty of several questions. Using measures derived from mouse movements, we predict whether respondents have answered the easy or difficult version of a question, using and comparing several state-of-the-art supervised learning methods. We have also developed a personalization method that adjusts for respondents' baseline mouse behavior and evaluate its performance. For all three manipulated survey questions, we find that including the full set of mouse movement measures and accounting for individual differences in these measures improve prediction performance over response-time-only models. [ABSTRACT FROM AUTHOR]

• Harirud River Basin Inspected for dam site suitability and runoff management. • 13 criteria used in the MCA, and 4 criteria used for the final site selection. • WLC Model yielded 12.31% highly, 21.0% moderately, and 66.68% unsuitable sites. • Optimization of series of eight dams resulted in storage of entire basin runoff. • Existing dams achieved accuracies of 84.65% for Salma and 88.08% for Pashdan. The intensification of drought conditions in arid and semi-arid regions coupled with the growing population increased water demands (domestic, and irrigation). It signifies the importance of addressing the pressing need for sustainable water resource management. Seventeen criteria spanning topographical, hydrological, geological, environmental, and socio-economic categories, were used for dam site suitability analysis for Harirud River Basin, Afghanistan. Weighted Linear Combination (WLC) model and Geographic Information System (GIS) were employed to perform multi-criteria analysis (MCA) method. The results revealed that the final suitability map exposed 12.31 % highly suitable, 21.0 % moderately suitable, and 66.68 % non-suitable sites, totaling 125.8 km, 214.6 km, and 681.4 km, respectively. Segmentation accuracy assessment was utilized to evaluate the existing dam's accuracies. The study revealed that accuracies of 84.65 % for the Salma Dam and 88.08 % for the Pashdan Dam were achieved, respectively. Six new dams, along with the existing two, formed the series of eight dams. The dam series were optimized for the total annual runoff of (1,600,000,000 m3), and the final dam heights were determined as follows: 107.5 m, 42 m, 100 m, 23.9 m, 30 m, 18 m, 23.44 m, and 22.65 m for dams (1 to 8), respectively. The study underscores the methodology's superiority in site suitability analysis and runoff optimization, while recommending future focus on observational data and higher-resolution digital elevation model (DEM) files for enhanced precision, environmental impact assessment, climate change considerations, social and economic implications, improved data resolution, and transboundary cooperation. [ABSTRACT FROM AUTHOR]

Numerical modelling of water flow allows for the prediction of rainwater partitioning into evaporation, deep drainage, and transpiration for different seasonal crop and soil type scenarios. We proposed and tested a single indicator for drainage estimation, the soil drainability index (SDI) based on the near saturated hydraulic conductivity of each layer. We studied rainfall partitioning for eight soils from Brazil and seven different real and generated weather data under scenarios without crop and with a permanent grass cover with three rooting depths, using the HYDRUS-1D model. The SDI showed a good correlation to simulated drainage of the soils. Moreover, well-trained supervised machine-learning methods, including the linear and stepwise linear models (LM, SWLM), besides ensemble regression with boosting and bagging algorithm (ENS-LB, ENS-B), support vector machines (SVMs), and Gaussian process regression (GPR), predicted monthly drainage from bare soil (BS) and grass covered lands (G) using soil–plant–atmosphere parameters (i.e., SDI, monthly precipitation, and evapotranspiration or transpiration). The RMSE values for testing data in BS and G were low, around 1.2 and 1.5 cm month−1 for all methods.

Tissue classification using computer aided diagnosis can help automated decision making to aid clinical diagnosis. Classification of breast tissue based on spectral features of impedance loci has frequently been done to classify malignant tissue with further requirement of more complex classification methodologies needed to improve the characterisation. In current study, tissue classification is done using in vivo electrical impedance data of 18 human subjects, from four quadrants of breast, palm, nail, arm, bicep and classified using algorithms involving machine learning methodologies, specifically support vector machines (SVMs) that are supervised learning models. They consist of learning algorithms based on the principal of structural risk minimisation. Two methodologies of SVM have been used in this study: with data binning and data pruning and without data binning and data pruning. Data binning and data pruning have improved the sensitivity of the SVM from 76.76 to 89.23%, but the specificity has decreased from 76.23 to 74.15%. This is a pilot study towards testing the reliability of the developed electrical impedance measuring system and developing a data mining‐based decision making system into an electrical impedance spectroscopy system, to help users (physicians) with tissue classification leading to reliable objective decision making. [ABSTRACT FROM AUTHOR]