Euclidean Distance-based Method Research Articles

Evaluation of priority index for enhancing the seismic resilience of tunnels

Tunnels are essential infrastructure elements, offering significant economic, social, and environmental benefits. However, they are particularly vulnerable to seismic risks, especially in regions with high seismic activity. Given their importance in terms of safety, economy, and social factors, it is crucial for the relevant authorities to prepare for natural and human-made disasters by enhancing the seismic resilience of tunnels. In Algeria, where the number of tunnels is high, this necessity is particularly urgent.This article proposes a prioritization model aimed at improving the seismic resilience of tunnels, based on the expertise of specialists and structured into three main steps. The first step involves identifying key criteria, including the tunnel's condition, design phase, availability of alternative routes, social importance, geometric shape, and seismic zone. The second step applies the Analytic Hierarchy Process (AHP) to calculate the weights of the criteria and incorporates expert opinions through the Euclidean Distance-Based Aggregation Method (EDBAM). Finally, the third step establishes the Priority Index for Tunnel Seismic Resilience Improvement (PIT).The model was tested on five tunnels, and the classification obtained based on the priority index was found to be well aligned with the observed reality on the ground.

Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data

ABSTRACT Artificial intelligence (AI) began to make its way into geoinformation science several decades ago and since then has constantly brought forth new cutting-edge approaches for diverse geographic use cases. AI and deep learning methods have become essential approaches for land use and land cover (LULC) classifications, which are important in urban planning and regional management. While the extraction of LULC information from multispectral satellite images has been a well-studied part of past and present research, only a few studies emerged about the recovery of spectral properties from LULC information. Estimates of the spectral characteristics of LULC categories could enrich LULC forecasting models by providing necessary information to delineate vegetation indices or microclimatic parameters. We train two identical Conditional Generative Adversarial Networks (CGAN) to synthesize a multispectral Sentinel-2 image based on different combinations of open-source LULC data sets. Large-scale synthetic multispectral satellite images of the administrative region of Bonn and Rhein-Sieg in Germany are generated with a Euclidean distance-based patch-fusion method. The approach generated a realistic-looking satellite image without noticeable seams between patch borders. Based on several metrics, such as difference calculations, the spectral information divergence (SID), and the Fréchet inception distance (FID), we evaluate the resulting images. The models reach mean SIDs as low as 0.026 for urban fabrics and forests and FIDs below 90 for bands B2 and B5 showing that the CGAN is capable of synthesizing distinct synthetic features matching with features typical for respective LULC categories and manages to mimic multispectral signatures. The method used in this paper to generate large-scale synthetic multispectral satellite images can be used as an approach to support scenario-oriented sustainable urban planning.

Planning location of parcel lockers using group Analytic Hierarchy Process in Spherical Fuzzy environment

Parcel lockers have emerged as an effective solution to address inefficiencies in last-mile delivery. However, the selection of an appropriate location plays a crucial role in ensuring optimal service and enhanced logistic performance. To address this challenge, this work aims to define the best location for parcel lockers in the capital of Ireland, Dublin by conducting a novel decision-making model. The Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) has been adopted to deal with the uncertainty in the decision maker preferences, while the Euclidean Distance-Based Aggregation Method (EDBAM) has been conducted to aggregate the decision makers preferences. The proposed integrated model is an applicable approach to elude AHP deterministic disadvantages, moreover, it is the first application of SF-AHP and EDBAM to select parcel lockers’ location. The findings, including the identification of the top three optimal locations, namely “Post office,” “On commercial area,” and “High population density area,” along with their respective weight scores, provide valuable insights for policymakers making strategic decisions regarding parcel locker placements in Dublin city.

Addressing last-mile delivery challenges by using euclidean distance-based aggregation within spherical Fuzzy group decision-making

Optimal location selection plays a crucial role in enhancing logistic performance and addressing inefficiencies in last-mile delivery. Parcel lockers have emerged as an efficient solution in this regard. In the context of optimizing last-mile logistics in Dublin, Ireland, this research strives to identify the ideal locations for parcel lockers. It does so by pioneering the application of a unique decision-making model. To navigate the inherent uncertainties in decision-makers' preferences, the study harnesses the power of the Analytic Hierarchy Process (AHP) in Spherical Fuzzy environment with the Euclidean Distance-Based Aggregation Method (EDBAM) for the selection of parcel locker locations. The harmonious integration of these methodologies serves to mitigate the deterministic shortcomings typically associated with the AHP approach, presenting an innovative and robust solution for enhancing last-mile delivery efficiency. The outcomes of this research will provide valuable insights to policymakers in making informed strategic decisions regarding the selection of parcel locker locations in Dublin City.

Development of predictive QSAR models for the substrates/inhibitors of OATP1B1 by deep neural networks

The organic anion transporting polypeptide 1B1 (OATP1B1) is an important hepatic uptake transporter. Inhibition of its normal function could lead to drug-drug interactions. In silico prediction is an effective means to identify potential OATP1B1 inhibitors and quantitative structure-activity relationship (QSAR) modeling is extensively used. As the structures of OATP1B1 substrates/inhibitors are quite diverse, machine learning based methods should be a good option for their QSAR analysis. In the present study, deep neural networks (DNNs) were employed to develop QSAR models for the substrates/inhibitors of OATP1B1 with different molecular fingerprints. Our results showed that QSAR models based on 4-hidden layer DNNs and ECFP4/FCFP4 fingerprints had the best generalization performance. The correlation coefficients (R2) of test set for ECFP4 and FCFP4 models were 0.641 and 0.653, respectively. Model application domain (AD) was calculated with Euclidean distance-based method, and AD could improve the performance of ECFP4 model but has little effect on FCFP4 model. Finally, the prediction of additional 8 compounds that not included in the data set further demonstrated that our QSAR models had a good predictive ability (averaged prediction accuracy >92%). The developed QSAR models could be used to screen large data sets and discover novel inhibitors for OATP1B1.

In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods.

Liver microsomal stability is an important property considered for the screening of drug candidates in the early stage of drug development. Determination of hepatic metabolic stability can be performed by an in vitro assay, but it requires quite a few resources and time. In recent years, machine learning methods have made much progress. Therefore, development of computational models to predict liver microsomal stability is highly desirable in the drug discovery process. In this study, the in silico classification models for the prediction of the metabolic stability of compounds in rat and human liver microsomes were constructed by the conventional machine learning and deep learning methods. The performance of the models was evaluated using the test and external sets. For the rat liver microsomes (RLM) stability, the best model yielded the AUC values of 0.84 and 0.71 on the test and external validation sets, respectively. For the human liver microsome (HLM) stability, the best model exhibited the AUC values of 0.86 and 0.77 on the test and external validation sets, respectively. In addition, several important substructure fragments were detected using information gain and frequency substructure analysis methods. The applicability domain of the models was defined using the Euclidean distance-based method. We anticipate that our results would be helpful for the prediction of liver microsomal stability of compounds in the early stage of drug discovery.

Distance-based aggregation in group AHP

ABSTRACT The aggregation of evaluators’ preferences is a key problem in group decision making. We examine the recently proposed distance-based techniques and compare their efficiency to the traditional aggregation of individual preferences (AIP) methods in simulated Analytic Hierarchy Process (AHP) cases. We use the Kendall W statistic to measure the rank correlation among the individual priority vectors of the group and the common priority vector for the different aggregation approaches. Extensive simulations (altogether 88000 cases) show that both the Euclidean Distance-Based Aggregation Method (EDBAM) and the Aitchison Distance-Based Aggregation Method significantly outperform the traditional techniques in case of smaller and mid-sized priority vectors (at most six items to be compared). However, EDBAM outperform the AIP methods for all dimensions that is conventionally used in AHP, and its computation time is also low.

Euclidean Distance-Based Method for Fault Detection and Classification in Transmission Lines

Euclidean Distance-Based Method for Fault Detection and Classification in Transmission Lines

Comparing aggregation methods in large-scale group AHP: Time for the shift to distance-based aggregation

This paper aims to compare the efficiency of the conventional aggregation methods and the new, distance-based aggregation techniques in simulated and real-world group AHP cases. For the comparison, we not only applied rank correlation methods, but also examined the compatibility among the individual priority vectors of the group and the created common priority vector in the different consensus creation approaches. Results have shown that in small dimensions, both Euclidean Distance-Based Aggregation Method (EDBAM) and Aitchison Distance-Based Aggregation Method (ADBAM) outperform significantly the conventional techniques. In large dimensions, the dominance of EDBAM remains. Since the computational time of the proposed methods (especially EDBAM) is low and EDBAM maintains its efficiency in large-scale group AHP (proven by 96.000 simulation cases) in every possible dimension within the AHP domain, we can state in case of high number of evaluators, distance-based aggregation is a better approach than the conventional methods.

Euclidean distance stratified random sampling based clustering model for big data mining

Big data mining is related to large-scale data analysis and faces computational cost-related challenges due to the exponential growth of digital technologies. Classical data mining algorithms suffer from computational deficiency, memory utilization, resource optimization, scale-up, and speed-up related challenges in big data mining. Sampling is one of the most effective data reduction techniques that reduces the computational cost, improves scalability and computational speed with high efficiency for any data mining algorithm in single and multiple machine execution environments. This study suggested a Euclidean distance-based stratum method for stratum creation and a stratified random sampling-based big data mining model using the K-Means clustering (SSK-Means) algorithm in a single machine execution environment. The performance of the SSK-Means algorithm has achieved better cluster quality, speed-up, scale-up, and memory utilization against the random sampling-based K-Means and classical K-Means algorithms using silhouette coefficient, Davies Bouldin index, Calinski Harabasz index, execution time, and speedup ratio internal measures.

Educational Information System Optimization for Artificial Intelligence Teaching Strategies

Under the background of the information age, scientific research and engineering practice have developed vigorously, resulting in many complex optimization problems that are difficult to solve. How to design more effective optimization methods has become the focus of urgent solutions in many academic fields. Under the guidance of such demand, intelligent optimization algorithms have emerged. This article analyzes and optimizes the modern artificial intelligence teaching information system in detail. On the basis of determining the network architecture, a detailed demand analysis was carried out, and the overall structure optimization of the network was given; the business process and data flow of the main modules of the website (resource center module and collaborative learning module) were optimized. In order to further enhance the local search ability of the algorithm, a multiclass interactive optimization algorithm is proposed in combination with the Euclidean distance-based clustering method, which changes the teaching mode from “one-person teaching” to “multiperson teaching.” This clustering method has lower complexity and is beneficial to enhance the utilization of neighborhood information. At the same time, in order to enhance the diversity of the population and strengthen the connection between the subgroups, after the teaching phase, the worst students in each subgroup are allowed to learn from the best teachers of the population, and after the learning phase, individuals in a random subgroup are allowed to learn from other subgroups. The algorithm was tested in the experimental environment of unconstrained, constrained, and an engineering problem. From the test results, it can be seen that the algorithm is not easy to fall into the local optimum. Compared with other algorithms, the solution accuracy is higher and the stability is better. And it performed well in engineering optimization problems, thus verifying the effectiveness of the strategy.

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data.

The current exponential increase of spatiotemporally explicit data streams from satellitebased Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that: (i) retrieval accuracy of AL-optimized training data sets outperformed models trained over large randomly sampled data sets, and (ii) Euclidean distance-based (EBD) diversity method tends to be the most efficient AL technique in terms of accuracy and computational demand. Additionally, a case study is presented based on experimental data employing both uncertainty and diversity AL criteria. Hereby, a a simulated training data base by the PROSAIL-PRO canopy RTM is used to demonstrate the benefit of AL techniques for the estimation of total leaf carotenoid content (Cxc) and leaf water content (Cw). Gaussian process regression (GPR) was incorporated to minimize and optimize the training data set with AL. Training the GPR algorithm on optimally AL-based sampled data sets led to improved variable retrievals compared to training on full data pools, which is further demonstrated on a mapping example. From these findings we can recommend the use of AL-based sub-sampling procedures to select the most informative samples out of large training data pools. This will not only optimize regression accuracy due to exclusion of redundant information, but also speed up processing time and reduce final model size of kernel-based machine learning regression algorithms, such as GPR. With this study we want to encourage further testing and implementation of AL sampling methods for hybrid retrieval workflows. AL can contribute to the solution of regression problems within the framework of operational vegetation monitoring using satellite imaging spectroscopy data, and may strongly facilitate data processing for cloud-computing platforms.

Multiple sclerosis lesion detection in multimodal MRI using simple clustering-based segmentation and classification

Abstract Background Multiple sclerosis (MS) is an immune-mediated inflammatory disease that attacks myelinated axons in the central nervous system, destroying myelin and axons to variable degrees and, resulting in significant physical disability. Magnetic resonance imaging (MRI) is useful in the diagnosis of MS, surpassing all other imaging techniques in terms of prediction accuracy. Depending on the number and location of lesions, however, the success of MR can vary significantly in terms of sensitivity and specificity in the diagnosis of MS. Adverse effects of various intensity and residual artifacts in the MRI data make it challenging to compute MS lesion volume to assess the progression of MS. Therefore, the development of robust and automated MS lesion detection methods has been a challenge. Objectives This study aims to develop a novel, robust, and simple image segmentation method to perform quantitative analysis of MS lesions from multimodal MRI data. Methods An algorithm based on a supervised minimum Euclidean distance-based clustering method employing three 2D MRI modalities, T1-weighted (T1w), fluid-attenuated inversion recovery (FLAIR), and T2-weighted (T2w) MRI was developed for classification of significant brain tissues and MS lesions. The developed method was applied to an MRI dataset from six MS patients. Results The developed method classifies various brain tissues and detects MS lesions with over 90% accuracy and specificity, and 62%–65% sensitivity, on average. Conclusions Segmentation of different brain tissues using our proposed algorithm results in superior MS lesion-detection accuracy, comparable with the recent deep-learning classification results in the literature.

Mapping ilmenite deposit in Pulmudai, Sri Lanka using a hyperspectral imaging-based surface mineral mapping method

Mineral detection using remote sensing techniques is important since it saves the time and effort of carrying out manual land surveys. In this paper a novel algorithm, which can be used to detect ilmenite using hyperspectral image analysis is discussed. To investigate this task, a hyperspectral image obtained from the Earth Observing-1 (EO-1) satellite’s Hyperion sensor was used. In the proposed algorithm, first, principal component analysis (PCA) was used for dimensionality reduction and an Euclidean distance-based method was used to extract the pixels containing soil. Thereafter, lab spectral data of typical ilmenite deposits were considered as the reference and a correlation factor analysis was carried out to determine the soil pixels, which are most likely to contain ilmenite and those most unlikely to contain ilmenite. Using these two sets of pixels, a training set was constructed to apply Fisher’s discriminant analysis (FDA) in order to separate the dataset into two distinct classes – ilmenite and non-ilmenite. Based on the spectral similarity, each pixel of the image was classified under one of these classes. This paper also introduces a probability-based approach to obtain results that are more accurate. A probability density function was designed considering the spatial distribution of the mineral. Thereafter, classification was done considering the probability measure as well. Lab tests performed on the soil samples collected from the locations, which were detected by the algorithm validate that the algorithm is accurate.

Multi-Scale Mahalanobis Kernel-Based Support Vector Machine for Classification of High-Resolution Remote Sensing Images

Support vector machine (SVM) is a powerful cognitive and learning algorithm in the domain of pattern recognition and image classification. However, the generalization ability of SVM is limited when processing classification of high-resolution remote sensing images. One chief reason for this is that the Euclidean distance-based distance matrix in traditional SVM treats different samples equally and overlooks the global distribution of samples. To construct a more effective SVM-based classification method, this paper proposes a multi-scale Mahalanobis kernel-based SVM classifier. In this new method, we first introduce a Mahalanobis distance kernel to improve the global cognitive learning ability of SVM. Then, the Mahalanobis distance kernel is embedded to the multi-scale kernel learning (MSKL) to construct a novel multi-scale Mahalanobis kernel, in which the parameters are optimized by a bio-inspired algorithm, named differential evolution. Finally, the new method is extended to the classification of high-resolution remote sensing images based on the spatial-spectral features. The comparison experiments of five public UCI datasets and two high-resolution remote sensing images verify that the Mahalanobis distance-based method can obtain more accurate classification results than that of the Euclidean distance-based method. In addition, the proposed method produced the best classification results in all the experiments. The global cognitive learning ability of Mahalanobis distance-based method is stronger than that of the Euclidean distance-based method. In addition, this study indicates that the optimized MSKL are potential for the interpretation and understanding of complicated high-resolution remote sensing scene.

Visual tracking with online structural similarity-based weighted multiple instance learning

This paper presents an online adaptive tracker, which employs a novel weighted multiple instance learning (WMIL) approach.In the proposed tracker, both positive and negative sample importances are integrated into an online learning mechanism for improving tracking performance in challenging environments. The sample importance is computed based on a new measure, i.e., structural similarity (SSIM), instead of using the Euclidean distance. Moreover, a novel bag probability function, which adopts both positive and negative weighted instance probabilities, is designed. Furthermore, a novel efficient weak classifier selection solution is developed for the proposed tracker. Qualitative and quantitative experiments on 30 challenging image sequences show that the novel tracking algorithm, i.e., SSIM-WMIL tracker, performs favorably against the MIL and WMIL counterparts as well as other 13 recently-proposed state-of-the-art trackers in terms of accuracy, robustness and efficiency. In addition, the negative sample importance can be used to enhance the multiple instance learning, and the SSIM-based approach is capable of improving the multiple instance learning performance for object tracking when compared to the Euclidean distance-based method.

Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata

ABSTRACTBoth the acute toxicity and chronic toxicity data on aquatic organisms are indispensable parameters in the ecological risk assessment priority chemical screening process (e.g. persistent, bioaccumulative and toxic chemicals). However, most of the present modelling actions are focused on developing predictive models for the acute toxicity of chemicals to aquatic organisms. As regards chronic aquatic toxicity, considerable work is needed. The major objective of the present study was to construct in silico models for predicting chronic toxicity data for Daphnia magna and Pseudokirchneriella subcapitata. In the modelling, a set of chronic toxicity data was collected for D. magna (21 days no observed effect concentration (NOEC)) and P. subcapitata (72 h NOEC), respectively. Then, binary classification models were developed for D. magna and P. subcapitata by employing the k-nearest neighbour method (k-NN). The model assessment results indicated that the obtained optimum models had high accuracy, sensitivity and specificity. The model application domain was characterized by the Euclidean distance-based method. In the future, the data gap for other chemicals within the application domain on their chronic toxicity for D. magna and P. subcapitata could be filled using the models developed here.

Development of QSAR model for predicting the inclusion constants of organic chemicals with α-cyclodextrin.

Solubility is a crucial limiting factor in pharmaceutical research and contaminated site remediation. Cyclodextrin, with its structure of hydrophilic exterior and hydrophobic cavity, has a potential ability to enhance the hydrophobic chemical's solubility through the formation of host-guest complex. The stability of host-guest complex is often quantified by the inclusion constant. In this study, the logarithm of 1:1 α-cyclodextrin inclusion constants (log Kα) for 195 organic chemicals was collected. With this parameter as the endpoint, a quantitative structure-activity relationship (QSAR) model was developed using DRAGON descriptors and stepwise multiple linear regression analysis. The model statistics parameters indicated that the established model had a good determination coefficient of 0.857, a high cross-validation coefficient of 0.835, a low root mean square error of 0.380, together with the acceptable results of external validation, which indicate a satisfactory goodness-of-fit, robustness, and predictive ability of the model. Based on the screened eight descriptors, we propose an appropriate mechanism interpretation for the inclusion interaction. Additionally, the applicability domain of the current model was characterized by the Euclidean distance-based method and Williams plot, and results indicated that the model covered a large number of structurally diverse chemicals belonging to 13 different classes. Comparing with the previous reported models, this model has obvious advantages with a larger dataset, a higher value of correlation coefficient, and a wider application domain.

Development of chicken and fish muscle protein – Water partition coefficients predictive models for ionogenic and neutral organic chemicals

Muscle protein was one of critical accumulation protein for anthropogenic chemicals. However, few predictive models were constructed for muscle protein up to now. In addition, some ionizable chemicals classes e.g. sulfonates were not successfully modeled in previously models, indicating considerable work would be needed. The major objective of this study was to develop quantitative structure-activity relationship (QSAR) models for predicting the muscle protein-water partition coefficient (logKMP/w) of chicken and fish. In the modeling, the n-octanol/water distribution coefficient (logD), functional groups, atom-centred fragments and chemical form adjusted descriptors were used to construct the models. The application domain of the derived models was defined by the Euclidean distance-based method and Williams plot. The modeling results indicated that the determination coefficient (R2), leave-one out cross validation Q2 (Q2LOO) and bootstrapping coefficient (Q2BOOT) of the QSAR models for chicken and fish were 0.882 and 0.929, 0.844 and 0.906, 0.779 and 0.792, respectively, implying the models had good goodness-of-fit and robustness. The coefficient determination (R2EXT) and external validation coefficient (Q2EXT) of the validation set for the two models were 0.874 and 0.937, 0.869 and 0.915, respectively, indicating the models had good predictive ability. The predictor variables selected to construct the logKMP/w models of chicken and fish included logD, the function groups, and the fraction of the ionized species (δI). Considering the molecular descriptors used here can be calculated from their molecular structures directly, the developed models could be easily used to fill the logKMP/w data gap for other chemicals within the applicability domain.

Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water

Partition coefficients are vital parameters for measuring accurately the chemicals concentrations by passive sampling devices. Given the wide use of low density polyethylene (LDPE) film in passive sampling, we developed a theoretical linear solvation energy relationship (TLSER) model and a quantitative structure-activity relationship (QSAR) model for the prediction of the partition coefficient of chemicals between LDPE and water (Kpew). For chemicals with the octanol–water partition coefficient (log Kow) <8, a TLSER model with Vx (McGowan volume) and qA− (the most negative charge on O, N, S, X atoms) as descriptors was developed, but the model had relatively low determination coefficient (R2) and cross-validated coefficient (Q2). In order to further explore the theoretical mechanisms involved in the partition process, a QSAR model with four descriptors (MLOGP (Moriguchi octanol-water partition coeff.), P_VSA_s_3 (P_VSA-like on I-state, bin 3), Hy (hydrophilic factor) and NssO (number of atoms of type ssO)) was established, and statistical analysis indicated that the model had satisfactory goodness-of-fit, robustness and predictive ability. For chemicals with log KOW>8, a TLSER model with Vx and a QSAR model with MLOGP as descriptor were developed. This is the first paper to explore the models for highly hydrophobic chemicals. The applicability domain of the models, characterized by the Euclidean distance-based method and Williams plot, covered a large number of structurally diverse chemicals, which included nearly all the common hydrophobic organic compounds. Additionally, through mechanism interpretation, we explored the structural features those governing the partition behavior of chemicals between LDPE and water.