Optimal Selection Approach Research Articles

Multi-Criteria Decision-Making Approach for Optimal Energy Storage System Selection and Applications in Oman

This research aims to support the goals of Oman Vision 2040 by reducing the dependency on non-renewable energy resources and increasing the utilization of the national natural renewable energy resources. Selecting appropriate energy storage systems (ESSs) will play a key role in achieving this vision by enabling a greater integration of solar and other renewable energy. ESSs allow for solar power generated during daylight hours to be stored for use during peak demand periods. Additionally, the proposed framework provides guidance for large-scale ESS infrastructure planning and investments to support Oman’s renewable energy goals. As the global renewable energy market grows rapidly and Oman implements economic reforms, the ESS market is expected to flourish in Oman. In the near future, ESS is expected to contribute to lower electricity costs and enhance stability compared to traditional energy systems. While ESS technologies have been studied broadly, there is a lack of comprehensive analysis for optimal ESS selection tailored to Oman’s unique geographical, technical, and policy context. The main objective of this study is to provide a comprehensive evaluation of ESS options and identify the type(s) most suitable for integration with Oman’s national grid using a multi-criteria decision-making (MCDM) methodology. This study addresses this gap by applying the Hesitate Fuzzy Analytic Hierarchy Process (HF-AHP) and Hesitate Fuzzy VIKOR methods to assess alternative ESS technologies based on technical, economic, environmental, and social criteria specifically for Oman’s context. The analysis reveals pumped hydro energy storage (PHES) and compressed air energy storage (CAES) as the most appropriate solutions. The tailored selection framework aims to guide policy and infrastructure planning to determine investments for large-scale ESSs and provide a model for comprehensive ESS assessment in energy transition planning for countries with similar challenges.

A two-stage approach for secure node localizationand optimal route selection for enhanced performance in wireless sensor networks

A two-stage approach for secure node localizationand optimal route selection for enhanced performance in wireless sensor networks

Exploration of weak-PGML Method for Efficient Stability Control During Machining Operations

Stable control of the machining process can be difficult to maintain in production environments due to many factors, but especially uncertainty about selection of optimal machining parameters. The true underlying stability model for production machines on the shop floor is generally unknown, so parameters are typically selected by operators based on manufacturer recommendations or, in most cases, their accumulated shop floor experience. A class of methods referred to as physics-guided machine learning (PGML) offers a new approach for optimal parameter selection that incorporates theoretical knowledge about machining dynamics into the data-driven machine learning workflow. The focus of this paper is a version of PGML appropriate to shop floor operations in small and medium-sized machine shops (SMMs)—referred to as weak-PGML—that does not require a priori theoretical knowledge of machining dynamics to approximate the true underlying stability model for a particular machining scenario. Rather, the weak-PGML leverages the operator’s accumulated domain knowledge to uncover the true stability model and informs parameter selection. Weak-PGML reflects the limited resources and lack of technical expertise typical of most SMMs with legacy machines and a new generation of workforce without the accumulated experience of operators who have spent many years on the shop floor.

A search-and-fill strategy to code generation for complex software requirements

Context:The realm of software development has seen significant transformations with the rise of Low-Code Development (LCD) and the integration of Artificial Intelligence (AI), particularly large language models, into coding practices. The proliferation of open-source software also offers vast resources for developers. Objective:We aim to combine the benefits of modifying retrieved code with the use of an extensive code repository to tackle the challenges of complex control structures and multifunctional requirements in software development. Method:Our study introduces a Search-and-Fill strategy that utilizes natural language processing (NLP) to dissect complex software requirements. It extracts control structures and identifies atomic function points. By leveraging large-scale pre-trained models, the strategy searches for these elements to fill in the automatically transformed program structures derived from descriptions of control structures. This process generates a code snippet that includes program control structures and the implementations of various function points, thereby facilitating both code reuse and efficient development. Results:We have validated the effectiveness of our strategy in generating code snippets. For natural language requirements involving multifunctional complex structures, we constructed two datasets: the Basic Complex Requirements Dataset (BCRD) and the Advanced Complex Requirements Dataset (ACRD). These datasets are based on natural language descriptions and Python code that were randomly extracted and combined. For the code snippets to be generated, we achieved the best results with the ACRD dataset, with BLEU-4 scores reaching up to 0.6326 and TEDS scores peaking at 0.7807. Conclusion:The Search-and-Fill strategy successfully generates a comprehensive code snippets, integrating essential control structures and functions to streamline the development process. Experimental results substantiate our strategy’s efficacy in optimizing code reuse by effectively integrating preprocessing and selection optimization approach. Future research will focus on enhancing the recognition of complex software requirements and further refining the code snippets.

Integrating Feature Selection with Machine Learning for Accurate Reservoir Landslide Displacement Prediction

Accurate prediction of reservoir landslide displacements is crucial for early warning and hazard prevention. Current machine learning (ML) paradigms for predicting landslide displacement demonstrate superior performance, while often relying on various feature engineering techniques, such as decomposing into different temporal lags and feature selection. This study investigates the impact of various feature selection techniques on the performance of ML algorithms for landslide displacement prediction. The Shuping and Baishuihe landslides in China’s Three Gorges Reservoir Area are used to comprehensively benchmark four prevalent ML algorithms. Both static ML models, including backpropagation neural network (BPNN), support vector machine (SVM), and dynamic models, such as long short-term memory (LSTM), and gated recurrent unit (GRU), are included. Each ML model is evaluated under three feature engineering techniques: raw multivariate time series, and feature selection under maximal information coefficient-partial autocorrelation function (MIC-PACF), or grey relational analysis-PACF (GRA-PACF). The results demonstrate that appropriate feature selection methods could significantly improve the performance of static ML models. In contrast, dynamic models effectively leverage inherent capabilities in capturing temporal dynamics within raw multivariate time series, seeing marginal gains with extensive feature engineering compared to no feature selection strategy. The optimal feature selection approach varies based on the ML model and specific landslide, highlighting the importance of case-specific assessments. The findings in this study offer guidance on integrating feature selection techniques with different machine learning models to maximize the robustness and generalizability of data-driven landslide displacement prediction frameworks.

Automatic Knowledge-driven Approach for Optimal Service Selection based on ELECTRE III and Quality-Aware Services

Automatic Knowledge-driven Approach for Optimal Service Selection based on ELECTRE III and Quality-Aware Services

Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis

<p>Healthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each combined method has been applied to the machine learning algorithms to find the best classifier for prognosis. The various performance metrices has been calculated for all the combined feature selection methods for logistic regression and support vector machine and found that for precise classification could be done using recursive elimination feature selection method with LASSO applied to logistic regression achieved a better performance than all other combined methods with high accuracy, sensitivity and high area under curve. Decision has been taken by data analytics that RFE+LASSO using LR feature selection method will provide an overall better performance for IoT based medical heart disease dataset after comparing all other combined methods with LR and SVM classifiers<em>. </em></p>

A GIS-based catastrophe approach for optimal site selection for installation of solar power plants, East Azerbaijan province case study, Iran.

Due to depletion of fossil fuels and environmental issues, renewable energy consumption is increasingly growing. Solar energy as the most abundant renewable energy source available is becoming more popular around the world. In the current study, the optimal sites for solar photovoltaic power plants in East Azerbaijan province, Northwest Iran, were investigated. A total of 17 variables were categorized into four groups: climatic, geomorphological, environmental, and access-economic. In order to integrate the variables, a model based on catastrophe theory in the context of GIS was applied. The relative importance and weight of the criteria are computed based on the internal mechanism of the catastrophic system, thus greatly reducing subjectivism and uncertainties of the decision-making process. Five optimal sites located in the western part of the province within the counties of Malekan, Bonab, Ajabshir, Shabestar, and Tabriz were identified as suitable sites for the construction of solar photovoltaic power plants, where there are ideal conditions in terms of many environmental-human variables such as high potential of solar energy, high sunshine hours, low relative humidity, suitable slope, poor vegetation, distance to protected areas, proximity to the population centers, excellent access to the roads and to the main power lines.

An innovative approach for optimal selection of pumped hydro energy storage systems to foster sustainable energy integration

The use of macro storage technologies has been widely studied in the literature with pumped hydro energy storage (PHES) emerging as the main option for its high stability and low environmental impact. Additionally, in recent years researchers have focused on combining a micro storage system with a photovoltaic and/or wind system that serves to increase self-consumption thereby reducing the load on the power grid. However, the selection of optimal micro system remains an open issue. This study thus proposes a methodology for selecting the optimal PHES system based on energy and economic analyses. The methodology was validated through a case study involving university buildings (University of Sannio) in Benevento - Italy, in which a photovoltaic system with peak power of about 142 kW was considered. The results obtained through a sensitivity analysis varying the total volume of reservoirs, showed an effective reduction in energy purchased from and sold to the grid. Subsequently the use of PHES system was compared with battery-based storage, commonly installed in building application. The energy produced from renewable sources delivered to the building increased by 25 % and 13 % for battery energy storage system (BESS) and PHES system, respectively. The economic analysis demonstrated the BESS to be most cost effective with a minimum levelized cost of storage of about 158 €/MWh, while the pumped hydro energy system showed attractive economic results only when the investment costs exclude the reservoirs (levelized cost of storage was 136.5 €/MWh).

Açık kalp ameliyatı geçiren ileri yaştaki hastalarda akut böbrek hasarı için risk faktörleri

Amaç: Bu çalışmadaki amacımız ileri yaştaki hasta grubunda açık kalp cerrahisi sonrasında akut böbrek yetmezliğinin risk
 faktörlerinin belirlenmesi ve doğru hasta seçimi yaparak bu hasta grubunda mortali-te ve morbiditeyi azalmaktır.
 Gereç ve Yöntemler: Ocak 2017-aralık 2022, 678 hasta retrospektif olarak incelenmiştir. Hastalar yaşlarına göre < 70 yaş = kontrol grubu, 70–80 yaş = septuagenarian grubu, ve > 80 yaş = octogenarian grubu olarak ayrılmıştır. Yaş, cinsiyet, demografik özellikler, Euroskor, preoperatif serum kreatinin dü-zeyi, postoperatif 1. aydaki serum kreatinin düzeyleri, diyaliz ihtiyacı ve bir yıllık mortalite parametre-leri analiz edilmiştir.
 Bulgular: Çalışmaya toplam 206 hasta dahi edilmiştir, bu hastalardan 94'u kontrol grubu (79 erkek), 29 hasta septuagenarian grubu (22 erkek), ve 83 hasta octogenarian grubu (70 erkek). Grupların preo-peratif serum kreatinin düzeylerinde istatistiksel olarak fark vardır. 26 hastanın renal replasman tedavi-si ihtiyacı olmuştur, bunların 6'si kontrol grubunda, 3'u septuagenarian grubunda, ve 17'si ise octoge-narian grubundadır. Renal replasman tedavisi açısından gruplar arasında istatistiksel fark vardır, ve re-nal replasman tedavisi alan hastaların 11 ölmüştür. Toplamda 22 hasta ölmüştür, bunların 4'u kontrol grubunda, 3'u septuagenarian grubunda, ve 15'i ise octogenarian grubundadır.
 Sonuç: İleri yaştaki hasta grubunda doğru hasta seçimi, titiz bir cerrahi ve kusursuz miyokardiyal ko-ruma mortalite ve morbiditeyi ciddi azaltmaktadır.

TOPSIS-ACO based feature selection for multi-label classification

This paper introduces an approach for optimal feature selection (FS) in multi-label (ML) data, where each sample can be associated with multiple class labels. The complexity of the feature space is significantly increased in comparison to single-label data, making decision-making challenging. Therefore, FS plays a crucial role in ML classification. The proposed method, called ML-TOPSIS-ACO, leverages the ‘Technique of Order Preference by Similarity to Ideal Solution’ (TOPSIS) as a Multi-Criteria Decision Making (MCDM) technique and ‘Ant Colony Optimization’ (ACO) as a meta-heuristic technique. The ML-TOPSIS-ACO method operates in two phases. In the first phase, Modified TOPSIS (MTOPSIS) is used to perform feature ranking and select the most important features. These selected features are then passed to Modified-ACO for feature re-ranking, aiming to identify the best subset of features for ML classification. To evaluate the effectiveness of ML-TOPSIS-ACO, experiments were conducted on nine benchmark datasets. The experimental results demonstrate that ML-TOPSIS-ACO outperforms other existing methods, achieving a winning percentage of 83%.

A convolutional neural network machine learning based navigation of underwater vehicles under limited communication

This paper proposes navigation of multiple autonomous underwater vehicles (AUVs) by employing machine learning approach for wide area surveys in underwater environment. Wide area survey in underwater environment is affected by low data rate.We consider two AUVs moving in formation through clustering followed by selection of optimal path that is affected by low data rate and limited acoustical underwater communication. A state compression approach using machine learning based acoustical localization and communication (ML-ALOC) is proposed to overcome the low data rate issue in which AUV states are approximated by Hierarchical clustering followed by an optimal selection approach using Convolutional Neural Network (CNN). The performance of the proposed state compression algorithm is compared with particle state compression algorithm based on K-Means clustering at each iteration followed by Akaike information criterion (AIC) pursuing extensive simulations, in which two AUVs navigate through trajectory. It is observed from the simulations that the proposed ML-ALOC system provides better estimates when compared with acoustical localization and communication (ALOC) system using particle clustering for state compression scheme.

Trend Decomposition for Temperature Compensation in a Radar-Based Structural Health Monitoring System of Wind Turbine Blades

The compensation of temperature is critical in every structural health monitoring (SHM) system for achieving maximum damage detection performance. This paper analyses a novel approach based on seasonal trend decomposition to eliminate the temperature effect in a radar-based SHM system for wind turbine blades that operates in the frequency band from 58 to 63.5 GHz. While the original seasonal trend decomposition searches for the trend of a periodic signal in its entirety, the new method uses a moving average to determine trends for each point of a periodic signal. The points of the seasonal signal no longer need to have the same trend. Based on the determined trends, the measurement signal can be corrected by temperature effects, providing accurate damage detection results under changing temperature conditions. The performance of the trend decomposition is demonstrated with experimental data obtained during a full-scale fatigue test of a 31 m long wind turbine blade subjected to ambient temperature variations. For comparison, the well-known optimal baseline selection (OBS) approach is used, which is based on multiple baseline measurements at different temperature conditions. The use of metrics, such as the contrast in damage indicators, enables the performance assessment of both methods.

Optimal scale selection approach for classification based on generalized multi-scale formal context

The classification of multi-scale data is an important research topic in granular computing. Its research goal is to determine the most appropriate scale and achieve better classification performance. However, determining the optimal scale is often a difficult problem due to lacking better metrics and optimization methods. In order to solve this problem, this paper proposes the optimal scale selection criteria for generalized multi-scale formal contexts. That is, the optimal scale uses the coarsest conditional attributes and finest decision attributes to optimize the combination of granularities of attributes. We combine these criteria with multi-objective optimization methods for developing an algorithm to fast compute the optimal scale. Experiments show that for the selected 14 data sets and 11 comparative classification methods, there are 9 classification methods with higher classification accuracies on more than 9 data sets. Therefore, the optimal scale selection method proposed in this paper is feasible and can effectively improve the performance of the classification method.

A fuzzy Euclidean taxicab distance-based MCDM approach for optimal personnel selection

A fuzzy Euclidean taxicab distance-based MCDM approach for optimal personnel selection

Hyperspectral band selection using a decomposition based multiobjective wrapper approach

Presently, sensors acquire data in a broad spectrum of contiguous bands with a high correlation in the available information among the neighboring bands. Due to the complexity of dealing with a large number of bands, selecting fewer representative spectral bands without compromising the information quantum has gained much attention. However, obtaining an optimum number of spectral bands without deteriorating the information value is a challenging problem. The present study proposes an evolutionary multiobjective optimization (EMO) based wrapper approach for optimal band selection. The efficacy of a wrapper approach depends on the performance of the underlying classifier. In this work, the simultaneous selection of optimal spectral bands and the hyperparameter of the classifier is carried out by applying a new mechanism that uses the power distribution-based interaction strategy for candidate generation. A bi-objective optimization problem is formulated with percentage reduction in bands and the corresponding classification accuracy as the objective functions. Five widely referenced hyperspectral datasets are used to assess the effectiveness of the proposed method. Experimental results demonstrate that the proposed technique selects a significantly lower number of bands while preserving information quantum thereby achieving better classification accuracy. Additionally, a comparison with other EMO techniques confirms the superior performance of the proposed method in finding a lower number of spectral bands without compromising the spectral information.

Robust multi-model mobile target localization scheme based on underwater acoustic sensor networks

Underwater mobile target localization based on underwater acoustic sensor networks (UASNs) is a critical application in marine fields. However, the mobility of underwater targets, asynchronous reception of localization information, stratification effects, and unknown measurement losses make UASN-assisted mobile target localization difficult. To address these issues, this study proposes a robust multi-model mobile target localization scheme (RMML) based on UASNs. The RMML suggests a simplified approach for optimal localization reference node selection based on the Cramér-Rao lower bound. This approach improves the localization accuracy of a mobile target by periodically selecting nodes that provide high-quality localization references. Using the localization reference provided by the selected nodes, a robust mobile target localization algorithm is developed based on an interacting multiple model and unscented Kalman filter. In this algorithm, a multipoint prediction method and ray tracing method are jointly proposed to improve the target state estimation accuracy under the asynchronous reception of localization information and the stratification effect. Maximum a posteriori probability estimation is used to estimate measurement loss, and pseudo-residuals are constructed based on the estimated measurement loss to improve the localization accuracy and robustness of the algorithm. Finally, extensive simulations and experiments are performed to verify the effectiveness of the RMML.

A GRA-based approach for optimal selection of SALBP-1 assembly line balancing heuristics

Assembly line balancing is a compulsory approach which enables mass production of similar parts with proper efficacy. Multiple heuristics are proposed in literature to solve single model assembly line balancing problems (SALBP-1). Previous work has focused on developing and evaluating assembly line balancing heuristics and apply them to different fields. Other work investigates the factors affecting the performance of particular assembly line balancing heuristic. However, there is still a need for a guideline for selecting the most suitable heuristic for a particular application with insights into multiple performance measures. This research presents a grey relational analysis (GRA)-based approach to aid process designers in selecting the heuristic generating that yields the best line balancing. Specifically, combinations of factors of cycle time, order strength, number of tasks, and time variability are considered as operational setups of the line balancing process. The solution of each setup is solved by each heuristic, and the performance of the solution is evaluated. Then, GRA is applied to rank the heuristics on basis of the resulting grey rational grades. The proposed approach provides useful insights into the combinations suitable for a particular heuristic. In this study, time variability and cycle time are shown to have the greatest impact on the overall performance of the considered heuristics.

Evaluation of Linear Programming and Optimal Contribution Selection Approaches for Long-Term Selection on Beef Cattle Breeding.

The optimized selection method can maximize the genetic gain in offspring under the premise of controlling the inbreeding level of the population. At present, genetic gain has been largely improved by using genomic selection in multiple farm animals. However, the design of the optimal selection method and assessment of its effects during long-term selection in beef cattle breeding are yet to be fully explored. In this study, a simulated beef cattle population was constructed, and 15 generations of simulated breeding were carried out using the linear programming breeding strategy (LP) and optimal contribution selection strategy (OCS), respectively. The truncation selection strategy (TS-I and TS-II) was used as the control. During the breeding process, genetic parameters including genetic gain, average kinship coefficient, QTL effect variance, and average observed heterozygosity were calculated and compared across generations. Our results showed that the LP method can significantly improve the genetic gain in the population, especially the genetic performance of the traits with high heritability and the traits with high weight in the breeding process, but the inbreeding level of the population is higher under LP strategy. Although the genetic gain in the population under the OCS strategy is lower than the TS-II strategy, this method can effectively control the inbreeding level of the population. Our findings also suggest that the LP and OCS method can be used as an effective means to improve genetic gain, while the OCS method is a more ideal method to obtain sustainable genetic gain during long-term selection.

Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis

The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon.