Search Results (141)

In this paper, we leverage multimodal data to classify minerals using a multi-stream neural network. In a previous study on the Tinto dataset, which consisted of a 3D hyperspectral point cloud from the open-pit mine Corta Atalaya in Spain, we successfully identified mineral classes by employing various deep learning models. However, this prior work solely relied on hyperspectral data as input for the deep learning models. In this study, we aim to enhance accuracy by incorporating multimodal data, which includes hyperspectral images, RGB images, and a 3D point cloud. To achieve this, we have adopted a graph-based neural network, known for its efficiency in aggregating local information, based on our past observations where it consistently performed well across different hyperspectral sensors. Subsequently, we constructed a multi-stream neural network tailored to handle multimodality. Additionally, we employed a channel attention module on the hyperspectral stream to fully exploit the spectral information within the hyperspectral data. Through the integration of multimodal data and a multi-stream neural network, we achieved a notable improvement in mineral classification accuracy: 19.2%, 4.4%, and 5.6% on the LWIR, SWIR, and VNIR datasets, respectively. Full article

Traffic flow prediction can provide important reference data for managers to maintain traffic order, and can also be based on personal travel plans for optimal route selection. On account of the development of sensors and data collection technology, large-scale road network historical data can be effectively used, but their high non-linearity makes it meaningful to establish effective prediction models. In this regard, this paper proposes a dual-stream cross AGFormer-GPT network with prompt engineering for traffic flow prediction, which integrates traffic occupancy and speed as two prompts into traffic flow in the form of cross-attention, and uniquely mines spatial correlation and temporal correlation information through the dual-stream cross structure, effectively combining the advantages of the adaptive graph neural network and large language model to improve prediction accuracy. The experimental results on two PeMS road network data sets have verified that the model has improved by about 1.2% in traffic prediction accuracy under different road networks. Full article

Deriving insight from data is a challenging task for researchers and practitioners, especially when working on spatio-temporal domains. If pattern searching is involved, the complications introduced by temporal data dimensions create additional obstacles, as traditional data mining techniques are insufficient to address spatio-temporal databases (STDBs). We hereby present a new algorithm, which we refer to as F1/FP, and can be described as a probabilistic version of the Minus-F1 algorithm to look for periodic patterns. To the best of our knowledge, no previous work has compared the most cited algorithms in the literature to look for periodic patterns—namely, Apriori, MS-Apriori, FP-Growth, Max-Subpattern, and PPA. Thus, we have carried out such comparisons and then evaluated our algorithm empirically using two datasets, showcasing its ability to handle different types of periodicity and data distributions. By conducting such a comprehensive comparative analysis, we have demonstrated that our newly proposed algorithm has a smaller complexity than the existing alternatives and speeds up the performance regardless of the size of the dataset. We expect our work to contribute greatly to the mining of astronomical data and the permanently growing online streams derived from social media. Full article

Water resources are scarce and difficult to manage in Kazakhstan, Central Asia (CA). Anthropic activities largely eliminated the Aral Sea. Afghanistan’s large-scale canal construction may eliminate life in the main stream of the Amu Darya River, CA. Kazakhstan’s HYRASIA ONE project, with a EUR 50 billion investment to produce green hydrogen, is targeted to withdraw water from the Caspian Sea. Kazakhstan, CA, requires sustainable programs that integrate both decision-makers’ and people’s behavior. For this paper, the authors investigated groundwater resources for sustainable use, including for consumption, and the potential for natural “white” hydrogen production from underground geological “factories”. Kazakhstan is rich in natural resources, such as iron-rich rocks, minerals, and uranium, which are necessary for serpentinization reactions and radiolysis decay in natural hydrogen production from underground water. Investigations of underground geological “factories” require substantial efforts in field data collection. A chemical analysis of 40 groundwater samples from the 97 wells surveyed and investigated in the T. Ryskulov, Zhambyl, Baizak and Zhualy districts of the Zhambyl region in South Kazakhstan in 2021–2022 was carried out. These samples were compared with previously collected water samples from the years 2020–2021. The compositions of groundwater samples were analyzed, revealing various concentrations of different minerals, natural geological rocks, and anthropogenic materials. South Kazakhstan is rich in natural mineral resources. As a result, mining companies extract resources in the Taraz–Zhanatas–Karatau and the Shu–Novotroitsk industrial areas. The most significant levels of minerals found in water samples were found in the territory of the Talas–Assinsky interfluve, where the main industrial mining enterprises are concentrated and the largest groundwater deposits have been explored. Groundwater compositions have direct connections to geological rocks. The geological rocks are confined to sandstones, siltstones, porphyrites, conglomerates, limestones, and metamorphic rocks. In observation wells, a number of components can be found in high concentrations (mg/L): sulfates—602.0 (MPC 500 mg/L); sodium—436.5 (MPC 200 mg/L); chlorine—465.4 (MPC 350 mg/L); lithium—0.18 (MPC 0.03 mg/L); boron—0.74 (MPC 0.5 mg/L); cadmium—0.002 (MPC 0.001 mg/L); strontium—15, 0 (MPC 7.0 mg/L); and TDS—1970 (MPC 1000). The high mineral contents in the water are natural and comprise minerals from geological sources, including iron-rich rocks, to uranium. Proper groundwater classifications for research investigations are required to separate potable groundwater resources, wells, and areas where underground geological “factories” producing natural “white” hydrogen could potentially be located. Our preliminary investigation results are presented with the aim of creating a large-scale targeted program to improve water sustainability in Kazakhstan, CA. Full article

The dynamic behavior of flood waves on rivers is essential to flood prediction. Natural flood waves are complex due to tributary inputs, rainfall variations, and overbank flows, so this study examines hydropower dam releases, which are simpler to analyze because channel effects are isolated. Successive arrival times and heights of peaks along 9 rivers with multiple stream gauges downstream of hydroelectric dams show that flow peaks typically become exponentially lower and wider with distance. The propagation velocity of peaks increases with water depth and channel slope but decreases with downstream distance and greater channel tortuosity. A rich hierarchy of velocities was found. Hydropower pulses progress at or in slight excess of the theoretical celerity, which is faster than the propagation rate of average natural floods, which in turn exceeds the mean velocity of water in the channel, yet the water moves faster than the peaks of record floods. The progressive changes to the height, shape, and velocity of hydropower flow peaks are simulated by the first analytical solution to the convolution integral for a rectangular source pulse that is based on diffusion-advection theory. Available data support some widely held expectations while refuting others. An expanded definition of “water mining” is proposed. Full article

Fairness-aware mining of data streams is a challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans in critical decision-making processes, e.g., hiring staff, assessing credit risk, etc. This calls for handling massive amounts of incoming information with minimal response delay while ensuring fair and high-quality decisions. Although deep learning has achieved success in various domains, its computational complexity may hinder real-time processing, making traditional algorithms more suitable. In this context, we propose a novel adaptation of Naïve Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance through multi-objective optimization (MOO). Class imbalance is an inherent problem in discrimination-aware learning paradigms. To deal with class imbalance, we propose a dynamic instance weighting module that gives more importance to new instances and less importance to obsolete instances based on their membership in a minority or majority class. We have conducted experiments on a range of streaming and static datasets and concluded that our proposed methodology outperforms existing state-of-the-art (SoTA) fairness-aware methods in terms of both discrimination score and balanced accuracy. Full article

An outlier, known as an error state, can bring valuable cognitive analytic results in many industrial applications. Aiming at detecting outliers as soon as they appear in data streams that continuously arrive from data sources, this paper presents an adaptive-kernel-based incremental scheme. Specifically, the Gaussian kernel function with an adaptive kernel width is employed to ensure smoothness in local measures and to improve discriminability between objects. The dynamical Gaussian kernel density is presented to describe the gradual process of changing density. When new data arrives, the method updates the relevant density measures of the affected objects to achieve outlier computation of the arrived object, which can significantly reduce the computational burden. Experiments are performed on five commonly used datasets, and experimental results illustrate that the proposed method is more effective and robust for incremental outlier mining automatically. Full article

Magnetite deposits represent important iron ore resources. Selective sorting of valuables from gangue and targeting of potential critical metals that can be recovered from waste streams must be implemented from the exploration and excavation steps onwards. Optical and scanning electron microscopy, electron microprobe analysis, dual-energy X-ray transmission, and computed tomography were applied to determine the mineralogy and classify the iron oxides of different iron ore types. These characteristics can be used for sorting at the exploration and extraction steps to reduce unvaluable materials at the loading and hauling steps, which contribute about 50% of the greenhouse gas emissions of the iron ore mining and mineral processing sector. These data also contribute to fine-tuning mineral processing parameters. Full article

Bird voice classification is a crucial issue in wild bird protection work. However, the existing strategies of static classification are always unable to achieve the desired outcomes in a dynamic data stream context, as the standard machine learning approaches mainly focus on static learning, which is not suitable for mining dynamic data and has the disadvantages of high computational overhead and hardware requirements. Therefore, these shortcomings greatly limit the application of standard machine learning approaches. This study aims to quickly and accurately distinguish bird species by their sounds in bird conservation work. For this reason, a novel concept-cognitive computing system (C3S) framework, namely, PyC3S, is proposed for bird sound classification in this paper. The proposed system uses feature fusion and concept-cognitive computing technology to construct a Python version of a dynamic bird song classification and recognition model on a dataset containing 50 species of birds. The experimental results show that the model achieves 92.77% accuracy, 92.26% precision, 92.25% recall, and a 92.41% F1-Score on the given 50 bird datasets, validating the effectiveness of our PyC3S compared to the state-of-the-art stream learning algorithms. Full article

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems. Full article

In the Bakony Region, in the mines of dolomite (dolostone) surfaces between the settlements of Márkó and Pétfürdő (Várpalota), in rubble beds exposed by them and with the consideration of these, the process of rubble formation is studied here in order to interpret the characteristics of rubble beds (different thicknesses and vertical changes in grain size) in the studied area. The mines in the area (differentiated between old-school/traditional mining or mechanical mining) were classified with the consideration of mining methods. Rubble varieties were differentiated, the bedding of rubble beds was studied along profiles, and the elevation difference between mines of mechanical mining and Stream Séd was determined. The calcareous content and structure compactness of 124 samples originating from dolomite, rubble, and non-rubble in the Bakony Region were compared. The data prove that the rubble developed by dissolution. Dissolution might have been caused by both meteoric water and karstwater. The rubble of mines excavated by traditional mining mainly developed to the effect of the dissolution effect of meteoric water (the rubble beds are of coarser and coarser grain size downwards), while the mines excavated by mechanical mining were formed to the dissolution effect of karstwater (the rubble beds are coarser and coarser upwards). The formation of rubble by karstwater origin has not been mentioned in the literature yet. However, dissolution of meteoric water origin may also take place in the case of the latter, and dissolution of karstwater origin also plays a role in the development of mines excavated by traditional mining. Full article

Blending of different ore types in the concentrator feed contributes significantly to maintaining a high recovery of valuable minerals with required grades in the concentrate. It is feasible to develop an ore-blending scheme over the life of a mine already in the design phase of the plant. In addition to ore characteristics, water quality is known to impact mineral recovery. A blending plan could also be developed for the different water streams of a future concentrator. This paper describes a novel modeling and simulation approach to predict metallurgical response combining ore types and water quality. The model is based on kinetic laboratory flotation test data, and it was tested on a case study. As a result, rougher flotation grade-recovery curves dependent on ore types and water quality are presented over the predicted life of the mine. The simulation results can be exploited in project design to maximize the recovery of valuable minerals and to ensure environmentally sound and profitable mining operations. Overall, the developed modeling tool can be applied widely for minerals processed by using froth flotation and water types available for kinetic laboratory flotation tests. Full article

This study was part of a project designed to simulate the long-term landform equilibrium of a rehabilitated mine site. The project utilized event Fine Suspended Sediment (FSS) fluxes in a receiving stream following a rainfall event as an indicator of landform stability. The aim of this study was to use HEC-HMS to determine sediment and discharge quantity upstream to determine how it affects the downstream development of the catchment landform, in terms of sediment changes and geomorphology. Thus, the study focused on hydrology and sediment modelling of the upper catchment with HEC-HMS (Hydrologic Engineering Centre-Hydrologic Modelling System) to determine the daily discharge and sediment output at the catchment outlet. HEC-HMS was used to calibrate the stream discharge and FSS quantities at the catchment outlet to observed continuous discharge and FSS values. The calibration of the HEC-HMS model was carried out for two water years and then the same model parameters were used to validate the model for a third water year. The catchment discharge and FSS were calibrated and validated for continuous rainfall events against observed discharge and FSS data at the catchment outlet. The model was then run for a projected rainfall of 50 years. The denudation rate predicted by the model was 0.0245 mm per year, which falls in the range previously determined for the region. The simulated sediment output was compared to the rainfall trends over the years. As a result, the sediment spikes following a rainfall-runoff event gradually decreased over time. Reducing FSS spikes indicates that the landform gradually attains stability. This modelling study can be used for long-term simulations to determine erosion equilibrium over the years and to quantify sediment yield in catchments for projected time periods. Full article

Shale gas is mostly produced using horizontal wells, since shale gas reservoirs have low porosity and permeability. It is challenging to predict a horizontal well’s critical liquid-carrying gas flow rate because horizontal wells have more complicated well structures and gas–liquid two-phase pipe flows than vertical wells. In addition, there are significant differences between shale gas reservoirs and conventional natural gas reservoirs as well as dynamic changes in the liquid production rate. The majority of critical liquid-carrying models currently in use in engineering are based on the force analysis of droplets in the gas stream or liquid film on the pipe wall in annular-mist flow in the vertical wellbore. However, they do not take into account the impact of changes to the entire wellbore structure and dynamic changes in the liquid production rate on gas–liquid two-phase flow patterns and liquid carrying in the wellbore. In order to perform the critical gas velocity test for liquid carrying in the entire wellbore of horizontal wells, a visual liquid-carrying simulation experimental device for the entire wellbore of horizontal wells and a high-speed camera were used in this study. The onset of liquid accumulation was analyzed comprehensively according to the overall increase of the wellbore liquid and the change of the system pressure. A modified K–H wave theory liquid-carrying model was developed by taking into account the impacts of liquid production rate and well inclination angle based on the experimental data, the K–H wave theory, the cross-section actual gas velocity, and the angle correction correlation formula. The improved liquid-carrying model is in good accordance with the test findings, according to the experimental results. In Shunan Gas Mine, Sichuan, China, there are eight deep shale gas wells, which produced a total of 25 sets of tests. The modified model was used to forecast and diagnose the liquid-carrying capacity in the entire wellbore of these wells. The diagnosis results are in good agreement with the actual production situation, and the coincidence rate is 92%. Full article

Hyperspectral images possess a continuous and analogous spectral nature, enabling the classification of distinctive information by analyzing the subtle variations between adjacent spectra. Meanwhile, a hyperspectral dataset includes redundant and noisy information in addition to larger dimensions, which is the primary barrier preventing its use for land cover categorization. Despite the excellent feature extraction capability exhibited by convolutional neural networks, its efficacy is restricted by the constrained receptive field and the inability to acquire long-range features due to the limited size of the convolutional kernels. We construct a dual-stream self-attention fusion network (DSSFN) that combines spectral and spatial information in order to achieve the deep mining of global information via a self-attention mechanism. In addition, dimensionality reduction is required to reduce redundant data and eliminate noisy bands, hence enhancing the performance of hyperspectral classification. A unique band selection algorithm is proposed in this study. This algorithm, which is based on a sliding window grouped normalized matching filter for nearby bands (SWGMF), can minimize the dimensionality of the data while preserving the corresponding spectral information. Comprehensive experiments are carried out on four well-known hyperspectral datasets, where the proposed DSSFN achieves higher classification results in terms of overall accuracy (OA), average accuracy (AA), and kappa than previous approaches. A variety of trials verify the superiority and huge potential of DSSFN. Full article