Predictive Approach Research Articles

From order to reorder: Assessment of the living environment of hydropower resettlement for just energy transition in China

Hydropower projects have contributed significantly to the world’s just energy transition, while at the cost of massive-scale resettlement and displacement. The principles of a just transition ensure that the low-carbon transition process is fair, inclusive and socially responsible. However, since hydropower resettlers are often involuntary, it incurs many socio-environmental disruptions for the affected. Whether the post-resttlement living environment of the resettlers can also be just and sustainable needs to be effectively evaluated. This paper establishes and validates an optimized deep neural network (DNN) approach for predictability and reliability. A comprehensive set of living environment development indicators is then used to assess the just transition in the living environment of resettlers, based on three typical hydropower station resettlement cases in the Wujiang River Basin in Guizhou Province, China. The results indicate that individual living condition, community development, and fairness in market and social participation have important impacts on the just transition of resettlers’ living environments. By summarizing the composite affiliation and score values of each sub-indicator, it is found that the Hongjiadu hydropower station has the lowest composite score of the resettlers’ living environment, ranging from 0.476 to 0.680, followed by the Dongfeng hydropower station, from 0.483 to 0.709. The resettlers of Qianzhong Hydropower Hub have the highest living environment status after resettlement, from 0.497 to 0.794. Although there are differences in the living environment scores of different hydropower stations, it is argued that the transition of the living environment in the stepped power stations along the Wujiang River is just and sustainable in the long term. This paper provides valuable insights into the just transition of hydropower and the living environment of the resettlers, so as to foster sustainable development policymaking.

A genetic algorithm-based feature selection approach for diabetes prediction

<p>Genetic algorithms have emerged as a powerful optimization technique for feature selection due to their ability to search through a vast feature space efficiently. This study discusses the importance of feature selection for prediction in healthcare and prominently focuses on diabetes mellitus. Feature selection is essential for improving the performance of prediction models, by finding significant features and removing unnecessary among them. The study aims to identify the most informative subset of features. Diabetes is a chronic metabolic disorder that poses significant health challenges worldwide. For the experiment, two datasets related to diabetes were downloaded from Kaggle and the results of both (datasets) with and without feature selection using the genetic algorithm were compared. Machine learning classifiers and genetic algorithms were combined to increase the precision of diabetes risk prediction. In the preprocessing phase, feature selection, machine learning classifiers, and performance metrics methods were applied to make this study feasible. The results of the experiment showed that genetic algorithm + logistic regression i.e., 80% (accuracy) works better for PIMA diabetes, and for Germany diabetes dataset genetic algorithm + random forest and genetic algorithm + K-Nearest Neighbor i.e., 98.5% performed better than other chosen classifiers. The researchers can better comprehend the importance of feature selection in healthcare through this study.</p>

Hepatitis C prediction using SVM, logistic regression and decision tree

Hepatitis C is an infection of the liver brought on by the HCV virus. In this condition, early diagnosis is challenging because of the delayed onset of symptoms. Predicting well enough can spare patients from permeant liver damage. The primary goal of this work is to use several machine learning methods to forecast this disease based on widely available and reasonably priced blood test data in order to diagnose and treat patients early on. Three machine learning techniques support vector machine (SVM), logistic regression, decision tree, has been applied on one dataset in this work. To find a suitable approach for illness prediction, the confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC), performances of different strategies have been assessed. The SVM model's overall accuracy is 0.92, the highest among the three models.

Advanced risk management models for supply chain finance

This review paper delves into the transformative potential of advanced risk management models in enhancing the resilience and efficiency of supply chain finance (SCF). By examining the application and development of Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, and blockchain technology, the paper highlights their role in transitioning from traditional reactive strategies to proactive and predictive risk management approaches. Despite the promising advantages, the paper also addresses the significant implementation challenges, model limitations, and regulatory and ethical considerations accompanying these technological advancements. Recommendations for effective deployment and areas for future research, particularly in overcoming existing hurdles and exploring emerging technologies, are also discussed. This comprehensive analysis aims to guide academics, industry professionals, and policymakers in harnessing advanced risk management models for a more robust SCF ecosystem.

Machine learning-based ensemble model for groundwater quality prediction: A case study

ABSTRACT Groundwater quality is vital for public health and environmental sustainability. As managing large datasets is challenging for traditional methods, this study combines the hidden Markov model (HMM) and the artificial neural network (ANN), a machine learning-based ensemble model to predict groundwater quality in Kanyakumari District, Tamil Nadu, India. In order to train the model, the acquired data is cleaned and normalized. HMM is used to find hidden patterns while the ANN architecture is used to forecast groundwater quality categories. Accuracy, precision, sensitivity, and F1-scores calculation are necessary to evaluate the model's performance. The effectiveness of the approach can be analyzed by k-fold cross-validation scores. The study demonstrates the effectiveness of the HMM–ANN approach in groundwater quality prediction with an accuracy of 97.41%. Thus, the research contributes to groundwater quality assessment by offering a unique methodology that facilitates informed decision-making for water resource management and environmental conservation.

NSGAN: a non-dominant sorting optimisation-based generative adversarial design framework for alloy discovery

The design and discovery of new materials is fundamental to advancing scientific and technological innovation. The recent emergence of the materials genome concept holds great promise in revolutionising materials science by enabling the systematic utilisation of data for efficient prediction and optimisation of ‘superior’ materials. However, the materials genome approach can be stymied by the vast complexity of design spaces, which often demand substantial computational resources and sophisticated data processing capabilities. To address these challenges, this work introduces a generative design framework called the non-dominant sorting optimisation-based generative adversarial networks (NSGAN). Capitalising on the synergies of genetic algorithms (GA) and generative adversarial networks (GANs), NSGAN provides a robust and efficient approach for tackling high-dimensional multi-objective optimisation design problems. To validate the efficacy of the proposed framework, we applied the model to a comprehensive dataset of aluminium alloys. Additionally, an online tool was created as a supplementary resource, offering a brief introduction to this innovative method for the wider scientific community. This study explores the potential of a predictive and data-driven approach in material design, indicating a promising pathway for widespread applications in the field of materials science.

A Stock Index Futures Price Prediction Approach Based on the MULTI-GARCH-LSTM Mixed Model

As a type of financial derivative, the price fluctuation of futures is influenced by a multitude of factors, including macroeconomic conditions, policy changes, and market sentiment. The interaction of these factors makes the future trend become complex and difficult to predict. However, for investors, the ability to accurately predict the future trend of stock index futures price is directly related to the correctness of investment decisions and investment returns. Therefore, predicting the stock index futures market remains a leading and critical issue in the field of finance. To improve the accuracy of predicting stock index futures price, this paper introduces an innovative forecasting method by combining the strengths of Long Short-Term Memory (LSTM) networks and various Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-family models namely, MULTI-GARCH-LSTM. This integrated approach is specifically designed to tackle the challenges posed by the nonstationary and nonlinear characteristics of stock index futures price series. This synergy not only enhances the model’s ability to capture a wide range of market behaviors but also significantly improves the precision of future price predictions, catering to the intricate nature of financial time series data. Initially, we extract insights into the volatility characteristics, such as the aggregation of volatility in futures closing prices, by formulating a model from the GARCH family. Subsequently, the LSTM model decodes the complex nonlinear relationships inherent in the futures price series and incorporates assimilated volatility characteristics to predict future prices. The efficacy of this model is validated by applying it to an authentic dataset of gold futures. The empirical findings demonstrate that the performance of our proposed MULTI-GARCH-LSTM hybrid model consistently surpasses that of the individual models, thereby confirming the model’s effectiveness and superior predictive capability.

Probabilistic Prediction of Floating Offshore Wind Turbine Platform Motions via Uncertainty Quantification and Information Integration

The accurate prediction of short-term platform motions in a real environment is crucial for the safe design, operation, and maintenance of floating offshore wind turbines (FOWTs). Numerical simulations of motions are typically associated with high uncertainties due to abstracted theoretical models, empirical parameters, initial environment parameters, etc. Therefore, it is necessary to integrate other sources of information associated with less uncertainty, e.g., monitoring data, for accurate predictions. In this paper, we propose a probabilistic prediction based on the Bayesian approach that logically integrates motion monitoring data with simulated motion predictions of FOWTs, considering uncertainties in the environment model, structural properties, motion prediction method, monitoring data, etc. The approach consists of constructing a prior probability density function (PDF) of a random variable (which characterizes the largest value of the initial motion response) via numerical simulations and a likelihood function based on platform motion monitoring data and deriving a posterior PDF of the random variable by Bayesian updating. Then, posterior distributions of short-term extreme motion responses are derived using the posterior PDF of the random variable, representing lower uncertainty and improved accuracy. A Metropolis–Hastings algorithm is adopted to obtain PDFs of complex probability distributions. The effectiveness of the approach is demonstrated on a real FOWT platform in Scotland. The proposed probabilistic prediction approach results in posterior distributions of short-term extreme platform motions associated with less uncertainty and higher accuracy, which is attributed to integrating prior knowledge with monitoring data.

Predictive machine learning approaches for perovskites properties using their chemical formula: towards the discovery of stable solar cells materials

Predictive machine learning approaches for perovskites properties using their chemical formula: towards the discovery of stable solar cells materials

THE ENVIRONMENTAL AWARENESS LEVEL AMONG SAUDI WOMEN AND ITS RELATIONSHIP TO SUSTAINABLE THINKING

Recognizing environmental concerns is crucial for fostering a mindset and implementing behaviors that contribute to sustainability. Individuals with good environmental awareness engage well in environmentally supportive behaviors and think in ways that contribute to a sustainable future. The current research aims to assess the level of environmental awareness among Saudi women and unveil the relationship between the level of environmental awareness and sustainable thinking. Additionally, the research seeks to explore the possibility of predicting sustainable thinking based on the level of environmental awareness. A questionnaire on environmental awareness and a questionnaire on sustainable thinking were applied to 176 female teachers working in the Wadi Dawasir governorate educational administration. The study utilised a descriptive-analytical methodology, namely the correlational predictive approach, to examine the relationship between the research variables and make predictions about their associations. The research results indicated a high level of environmental awareness among the research sample and a high level of sustainable thinking. Furthermore, the findings revealed a statistically significant positive correlational relationship between the level of environmental awareness and sustainable thinking. The results also demonstrated the possibility of predicting sustainable thinking based on the level of environmental awareness in the research sample.

THE ENVIRONMENTAL AWARENESS LEVEL AMONG SAUDI WOMEN AND ITS RELATIONSHIP TO SUSTAINABLE THINKING

Recognizing environmental concerns is crucial for fostering a mindset and implementing behaviors that contribute to sustainability. Individuals with good environmental awareness engage well in environmentally supportive behaviors and think in ways that contribute to a sustainable future. The current research aims to assess the level of environmental awareness among Saudi women and unveil the relationship between the level of environmental awareness and sustainable thinking. Additionally, the research seeks to explore the possibility of predicting sustainable thinking based on the level of environmental awareness. A questionnaire on environmental awareness and a questionnaire on sustainable thinking were applied to 176 female teachers working in the Wadi Dawasir governorate educational administration. The study utilised a descriptive-analytical methodology, namely the correlational predictive approach, to examine the relationship between the research variables and make predictions about their associations. The research results indicated a high level of environmental awareness among the research sample and a high level of sustainable thinking. Furthermore, the findings revealed a statistically significant positive correlational relationship between the level of environmental awareness and sustainable thinking. The results also demonstrated the possibility of predicting sustainable thinking based on the level of environmental awareness in the research sample.

Performance prediction for network-level flexible pavements using an optimised ANN approach

ABSTRACT Considering that the road network shares traffic flow as a system, this study aimed to predict the network-level flexible pavement performance, using an optimised artificial neural network (ANN) approach to predict the pavement performance of each individual road, and the Ford-Fulkerson algorithm to determine the weight coefficients of roads. ANNs were developed to predict distress conditions, functional conditions and structural performance using the inputs of the pavement age and structural, traffic and climatic conditions. ANNs were trained with Long-Term Pavement Performance (LTPP) program data, and almost all the coefficient of determination values between ANN outputs and measured results are larger than 0.9. This ANN approach was optimised by establishing grey models to provide predictive short-term performance data for training ANNs, and employing Kalman Filter to modify the long-term performance prediction. Applications in typical LTPP sections validated the effectiveness of the optimisation method. This study used the reduction in road network capacity due to the assumed unavailability of a certain road to quantify its role (weight coefficient), which was calculated using the Ford-Fulkerson algorithm. This network-level performance prediction approach was applied in a 25-expressways network in Shanghai, which validated the good generalisation capabilities of ANNs and feasibility of the Ford-Fulkerson algorithm.

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions.

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

Wavelet entropy and complexity–entropy curves approach for energy commodity price predictability amid the transition to alternative energy sources

In recent years, energy commodities have emerged as pivotal and widely debated subjects, driven by their profound influence on the global economy and their intricate interconnections. Moreover, the challenges stemming from the predictability of energy commodity prices have become a prominent and intensifying focus of discussion. To this aim, in this paper, we employ wavelet analysis with an entropy approach to investigate and evaluate fluctuations, low-frequency events, and rare events in energy commodity prices and the consequent predictability of such time series. In particular, wavelet analysis can differentiate high-frequency from low-frequency movements in a time series, and entropy is a valuable mathematical tool used to evaluate the state of disorder, randomness, or uncertainty in a time series. Specifically, we employ Rényi Entropy instead of the Shannon entropy because it allows for enhanced consideration of low-frequency events and spikes in a time series. Therefore, to analyze the predictability of a series, we use Wavelet Rényi Entropy (WRE) and the Rényi complexity–entropy curves by combining the wavelet transform with Rényi entropy. Finally, we apply our analysis to real financial data, including energy commodities and indices that can describe the transition to alternative energy resources.

Machine Learning based approach for Diabetes Prediction

Diabetes is an illness brought on by an excessive amount of glucose within the body. Ignorance of diabetes is no longer acceptable. If neglected, it may also result in more severe health concerns for a person, such as heart-related problems, renal problems, blood pressure, eye damage, and effects on other body organs. Insulin hormone is affected, which leads to abnormal crab metabolism and elevates blood sugar levels. According to the World Health Organization, 422 million people worldwide suffer with diabetes. low- and middle-class people being disproportionately affected. The condition is caused by the body producing insufficient amounts of insulin. Additionally, this might reach 490 billion by 2030. To benefit from this challenging job, we may apply ensemble techniques and system learning for classification on this image to forecast whether diabetes will be present in a dataset. When comparing one version to another, the accuracy varies depending on the model. The assignment provides the accurate or improved accuracy version, indicating that the model can effectively predict diabetes. Our findings demonstrate that random forested areas outperformed other system mastery strategies in terms of accuracy. Keywords: Classification Algorithms, Supervised Learning, Unsupervised Learning, Random forest

IDMIR: identification of dysregulated miRNAs associated with disease based on a miRNA-miRNA interaction network constructed through gene expression data.

Micro ribonucleic acids (miRNAs) play a pivotal role in governing the human transcriptome in various biological phenomena. Hence, the accumulation of miRNA expression dysregulation frequently assumes a noteworthy role in the initiation and progression of complex diseases. However, accurate identification of dysregulated miRNAs still faces challenges at the current stage. Several bioinformatics tools have recently emerged for forecasting the associations between miRNAs and diseases. Nonetheless, the existing reference tools mainly identify the miRNA-disease associations in a general state and fall short of pinpointing dysregulated miRNAs within a specific disease state. Additionally, no studies adequately consider miRNA-miRNA interactions (MMIs) when analyzing the miRNA-disease associations. Here, we introduced a systematic approach, called IDMIR, which enabled the identification of expression dysregulated miRNAs through an MMI network under the gene expression context, where the network's architecture was designed to implicitly connect miRNAs based on their shared biological functions within a particular disease context. The advantage of IDMIR is that it uses gene expression data for the identification of dysregulated miRNAs by analyzing variations in MMIs. We illustrated the excellent predictive power for dysregulated miRNAs of the IDMIR approach through data analysis on breast cancer and bladder urothelial cancer. IDMIR could surpass several existing miRNA-disease association prediction approaches through comparison. We believe the approach complements the deficiencies in predicting miRNA-disease association and may provide new insights and possibilities for diagnosing and treating diseases. The IDMIR approach is now available as a free R package on CRAN (https://CRAN.R-project.org/package=IDMIR).

From Time-Series to Hybrid Models: Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems.

RedCDR: Dual Relation Distillation for Cancer Drug Response Prediction.

Based on multi-omics data and drug information, predicting the response of cancer cell lines to drugs is a crucial area of research in modern oncology, as it can promote the development of personalized treatments. Despite the promising performance achieved by existing models, most of them overlook the variations among different omics and lack effective integration of multi-omics data. Moreover, the explicit modeling of cell line/drug attribute and cell line-drug association has not been thoroughly investigated in existing approaches. To address these issues, we propose RedCDR, a dual relation distillation model for cancer drug response (CDR) prediction. Specifically, a parallel dual-branch architecture is designed to enable both the independent learning and interactive fusion feasible for cell line/drug attribute and cell line-drug association information. To facilitate the adaptive interacting integration of multi-omics data, the proposed multi-omics encoder introduces the multiple similarity relations between cell lines and takes the importance of different omics data into account. To accomplish knowledge transfer from the two independent attribute and association branches to their fusion, a dual relation distillation mechanism consisting of representation distillation and prediction distillation is presented. Experiments conducted on the GDSC and CCLE datasets show that RedCDR outperforms previous state-of-the-art approaches in CDR prediction. The source code is available at https://github.com/mhxu1998/RedCDR.

A transfer learning-based hybrid model with LightGBM for smart grid short-term energy load prediction

Efficient energy management is crucial given 2024's 4.1% worldwide electricity demand increase. This urgency emphasizes the necessity for various, sustainable energy sources in distribution grids. Short-term load prediction approaches using probabilistic power generation and energy storage are crucial for energy usage prediction. Urban energy planners use simulation, probability optimization and modelling to create sustainable energy systems. This study offers a novel hybrid model for smart grids: short-term energy load prediction using transfer learning (TL) and optimized lightGBM (OLGBM). Our two-phase solution tackles Short-term Load Forecasting complexities. First, aberrant supplements and quick deviation selection eliminate missing values and identify key features during data pre-processing. Second, TL-OLGBM learns dynamic time scales and complex data patterns with Bayesian optimization of hyperparameters to improve forecasting accuracy. Additionally, our architecture easily combines the newest Smart and Green Technology, enabling energy system innovation. Comparative performance research shows that our technique outperforms similar models in mean absolute percentage error, accuracy and root mean square error. This hybrid model is a reliable short-term energy load forecast solution that fits the dynamic terrain of smart and green technology integration in modern energy systems.

Response prediction and probabilistic analysis of the vehicle-ballasted track system considering track irregularity based on long-short term memory neural network

It is necessary to efficiently and accurately predict the dynamic behavior of the train-track system subjected to massive random track geometric excitations for system evaluation and maintenance. This paper proposed an engineering practical approach for the train-track system behavior prediction by integrating the deep learning surrogate model-based long-short term memory (LSTM) and physical model of the train-ballasted track interaction (TBTI) system, and then the generalized probability density evolution method (GPDEM) is introduced to achieve the probabilistic assessment of the TBTI system under limited track irregularities, where the track irregularity probability model (TIPM) coupled with generalized-F discrepancy points sets is employed to compress the track irregularity sets to reduce the number of system responses (as training target) but without loss of the responses completeness, therefore, it can promote the efficiency and accuracy of training model. The prediction performance of the surrogate model is illustrated by comparing the time-frequency domain information, mean and standard deviation of the responses obtained by the physical model and surrogate model. The results indicated the proposed method can accurately predict the system response, especially the low and medium frequency response below 50 Hz, and improve the efficiency of stochastic analysis for the TBTI system by approximately 20 times. Furthermore, the applicability of the proposed method is further examined by investigating the influence of probability levels of track irregularity and train speeds on train performance. Its extended model is also implemented to predict the response of systems induced by any measured track irregularity in real-time, which promotes the generalization capability of the proposed surrogate model.