Information Mining Research Articles

Mining of heterogeneous time series information for predicting chlorophyll accumulation in oceans

Harmful algal blooms cause environmental harm, financial losses, and disease epidemics. It is also known that the algal blooms cannot be eradicated; hence the best option is to foresee their growth and regulate it. Machine learning algorithms can be used to forecast their presence and further classify the threat that each concentration level presents. In this research work, the dataset collected from Santa Monica, US region is analyzed and processed to predict algae concentration using machine learning algorithms. In this process, the machine learning models such as multiple linear regression, Regression Gradient Boosting Decision Tree (RGBDT), and Hidden Markov Model (HMM) are applied to predict the chlorophyll (Chl-a) content, which serves as a proxy for the presence of algae in the water. The obtained results show that for prediction, the Multilinear regression model outperforms the RGBDT (Regression Gradient Boosting Decision Tree) algorithm. Similarly, for modeling chlorophyll using HMM (Hidden Markov Model), parameter bbp555.00_sd is the best among parameters like aot443.00_sd, kd490.00_sd, poc_sd and pic_sd. The multiple linear regression model gave an adjusted R-squared error of 0.94 with the parameter pic_sd having the least VIF value of 1.78 followed by aot and bbp which have VIF<5 (2.28 and 4.95 respectively). The outcome of the HMM-based model represents the probability of the presence of chlorophyll given the presence of each of the variables individually. From the results, it is observed that bbp has the highest probability of 0.405 implying that there is a 40% chance of chlorophyll in the presence of bbp.

Intelligent Situation Awareness Based on the Fractal Dimension and Multidimensional Reconstruction

AbstractIn order to realize autonomy and intelligence for situation awareness, this paper proposes an intelligent situation awareness model based on fractional dimension information mining and multidimensional information reconstruction. First, some new concepts are proposed, the spatial situation perception is established by 3D reconstruction of the input fusion information, 4D reconstruction completes the situation comprehension and 5D reconstruction seeks the situation prediction. The three‐level situation estimation model is optimized to a more robust flexible model. Combined with the database system, the reasoning learning mechanism, and the diversified human‐machine interface concept, a basic framework of intelligent situation awareness is constructed. Second, the basic process of intelligent situation awareness is provided. Third, based on the flexible configuration method of the situation awareness system and the concept of unmanned weight determination for the situation awareness agents, an efficiency measurement model for the intelligent situation awareness model is also constructed, and an evaluation method for the consistency of multinode intelligent situation awareness is also provided. Fourth, the paper gives a typical electromagnetic situation estimation example for a drilling platform to explain and validate the concepts. Finally, several suggestions are put forward for the next construction of an intelligent situation awareness system.

A Machine Learning-Based Pipeline for the Extraction of Insights from Customer Reviews

The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. This paper presents a model that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector-embedding-based keyword extraction, and clustering. The elements of our model have been integrated and tailored to better meet the requirements of efficient information extraction and topic modeling of the extracted information for opinion mining. Our approach was validated and compared with other state-of-the-art methods using publicly available benchmark datasets. The results show that our system performs better than existing topic modeling and keyword extraction methods in this task.

Multi-granularity Cross Transformer Network for person re-identification

Person re-identification (Re-ID) aims to retrieve the same person in the gallery. Transformers have been introduced to the Re-ID task due to their excellent ability to model long-range dependency. However, due to the properties of the global attention mechanism, they are less effective in capturing the discriminative local semantics of pedestrians compared to convolutional operations. To address this issue, we present a Multi-granularity Cross Transformer Network (MCTN) that progressively learns salient features of different local structures in a global context. Specifically, we first utilize a Multi-granularity Convolutional Layer (MCL) to investigate salient pedestrian features at various granularities. On this basis, we propose a Pyramidal Cross Transformer learning layer (PCT), which contains a pyramidal division of pedestrian image feature maps, differentiated feature extraction of different parts of pedestrians, and cross attention to exploring the local–global relationship of the feature map. It allows effective mining of local information in the global structure from a coarse-to-fine perspective. Furthermore, to enhance the interaction between low-level detailed features and high-level semantic features, a Hierarchical Aggregation Strategy (HAS) is introduced to fuse features learned by cross attention learning at different stages. Pedestrian features learned in shallow layers will serve as global priors for semantics learning in deep layers. We evaluate our method on four large-scale Re-ID datasets, and the experimental results reveal that the proposed method outperforms the state-of-the-art methods.

CDYL for infrared and visible light image dense small object detection

To address the phenomenon of many small and hard-to-detect objects in infrared and visible light images, we propose an object detection algorithm CDYL (Convolution to Fully Connect-ed-Deformable Convolution You only Look once) based on the CFC-DC (Convolution to Fully Connected-Deformable Convolution) module. The core operator of CDYL is CFC-DC, making our model not only have a large effective receptive field in infrared and visible light images, but also have adaptive spatial aggregation conditioned by input and task information. As a result, the CDYL reduces the strict inductive bias of traditional CNNs and has long-range dependence for large kernel convolution as well as adaptive spatial aggregation, deeply mining of edge and correlation information in images to enhance sensitivity to small objects, thereby improving performance in dense small object detection tasks. In order to improve the ability of the CFC-DC module to perceive the detailed information of the image, we use the Mish activation function, which has a wider minima which improves the generalization. The effectiveness as well as the generalization of CDYL is evaluated on an infrared image dataset and an UAV image dataset, and it is compared with other state-of-the-art object detection algorithms. Compared to the baseline network YOLOv8l, our model achieved a 3.0% improvement in mAP0.5 in infrared image detection tasks and a 1.1% improvement in mAP0.5 in visible light image detection tasks. The experimental results show that the proposed algorithm achieves superior average precision values on both infrared and visible light images, while maintaining a light weight. Code is publicly available at https://github.com/yangzhu1/CDYL.

Deployment of Advanced and Intelligent Logistics Vehicles with Enhanced Tracking and Security Features

This study focuses on the implementation of modern and intelligent logistics vehicles equipped with advanced tracking and security features. In response to the evolving landscape of logistics management, the proposed system integrates cutting-edge technologies to enhance efficiency and ensure the security of the entire logistics process. The core component of this implementation is the incorporation of state-of-the-art tracking mechanisms, enabling real-time monitoring of vehicle locations and movements. Furthermore, the system addresses the paramount concern of security by introducing advanced security measures. Through the utilization of sophisticated tracking technologies and security protocols, the proposed logistics vehicles aim to safeguard both customer and provider data. The implementation includes the integration of QR code concepts, creating a binary image system that conceals sensitive information and ensures access only to authorized users. In addition to tracking and security, the study delves into the realm of information mining, employing techniques such as classification, clustering, and recommendation to extract meaningful patterns from vast datasets. Collaborative filtering techniques are incorporated to enhance customer experience by recommending services based on user preferences and historical data. This abstract encapsulates the comprehensive approach of deploying modern logistics vehicles, emphasizing their intelligence through advanced tracking, robust security measures, and data-driven insights. The proposed system aims to revolutionize logistics management, providing a seamless and secure experience for both customers and service providers in the dynamic logistics landscape.

Investigation of recent advances in exercise-enhanced knowledge tracing models

Exercising plays a significant role in Knowledge Tracing (KT), however, the majority of KT models struggle to effectively extract the abundant information embedded within students' exercise histories, leading to a prevalent issue of information erosion. Hence, it is vital for exploring methods to mine the data from the exercises to better forecast students' future performance. This paper is a review that aims to discuss recent improvements in KT models on mining information existing in exercises. This paper will briefly discuss DKT, a popular approach to knowledge tracing, and its limitation on mining information from exercises. And then, this review will introduce four exercise-enhanced knowledge tracing models including EERNN, EKT, HGKT and Concept-Aware Deep Knowledge Tracing. EERNN and EKT can track students knowledge acquisition by using vectors to represent knowledge concepts. HGKT goes a step further by examining the interconnectedness among different exercises based on EERNN and EKT. Concept-Aware DKT is an improvement of DKVMN, the other approach of KT, by considering the influence of knowledge concepts and corresponding exercises in more detail. Finally, the applications of exercise-enhanced KT models will be covered.

An effective neighbor information mining and fusion method for recommender systems based on generative adversarial network

In recommender system, the proportion of interacted items with users is extremely sparse compared to the total number of items. This data sparsity problem particularly affects collaborative filtering methods. Even in modern collaborative filtering recommendation models based on deep learning, effectively acquiring more neighbor information and exploring how to utilize this information efficiently remains a promising research area. This paper not only explores heuristic algorithms for neighbor selection but also extends the application of neighbor selection to the GAN framework and negative sampling. To capture user neighbors that contain more potential feature information, this paper proposes a neighbor strategy that integrates explicit and implicit user information. It aggregates reciprocal neighbors of users based on the fusion of their explicit and implicit information, aiming to capture user preference information through neighborhood information. Furthermore, by designing an MRNGAN model based on a twin-tower generator, this paper successfully embeds user neighborhood information as feature embedding into the generator of GAN. In addition, this paper also proposes a heuristic negative sampling mechanism in the recall phase. It uses explicit and implicit information to fuse the nearest neighbors of items with the farthest neighbors of users. Negative samples that contain user neighborhood information are embedded into the discriminator of MRNGAN. The deep relationships between nearest and farthest neighbors are explored through the adversarial training of GAN, and their impact on recommendation performance is validated. The experimental results on three real-world datasets show that the MRNGAN model significantly improves the accuracy of Top-N recommendation.

DISTANCE CLASSES ON PHYSICAL TRAINING AT THE HEI IN COMPARANCE WITH TRADITIONAL FORM OF EDUCATION

The article examines the feasibility of a distance form of practical training from physical training to maintain the level of physical and mental health of students in the minds of the military and the results of training in the initial process of physical training in Bathroom (due to the desire and ability of students to swim in the pool) at a glance on the availability of sports equipment, group forms of spilkuvaniya and direct contact with the depositor. To achieve the highest objectives, the methods of pedagogical care, training, literature analysis and information mining, logical and mathematical - statistical methods were used.

Machine-to-Machine Visual Dialoguing with ChatGPT for Enriched Textual Image Description

Image captioning is a technique that enables the automatic extraction of natural language descriptions about the contents of an image. On the one hand, information in the form of natural language can enhance accessibility by reducing the expertise required to process, analyze, and exploit remote sensing images, while on the other, it provides a direct and general form of communication. However, image captioning is usually restricted to a single sentence, which barely describes the rich semantic information that typically characterizes remote sensing (RS) images. In this paper, we aim to move one step forward by proposing a captioning system that, mimicking human behavior, adopts dialogue as a tool to explore and dig for information, leading to more detailed and comprehensive descriptions of RS scenes. The system relies on a questions–answers scheme fed by a query image and summarizes the dialogue content with ChatGPT. Experiments carried out on two benchmark remote sensing datasets confirm the potential of such an approach in the context of semantic information mining. Strengths and weaknesses are highlighted and discussed, as well as some possible future developments.

Adaptive deep fusion neural network based soft sensor for industrial process

AbstractDeep neural networks have become an important tool for soft sensor modeling. However, common deep autoencoder networks are limited to mining the effective information of each input layer during hierarchical training, ignoring the loss of effective information in the original input data and accumulating it layer‐by‐layer, resulting in incomplete feature representation of the original input. At the same time, there is a lack of mining for temporal correlation between process samples and an adaptive mechanism to strengthen temporal related features, resulting in insufficient process information mining. In addition, deep neural networks generally have overfitting problems. To this end, an adaptive deep fusion neural network (ADFNN) method is proposed. This method reconstructs the original input data at each layer of the feature extraction network. By using the reconstructed original input error in pre‐training loss, it reduces the loss of effective information from the original input. Simultaneously, incorporating sliding windows and self‐attention mechanisms to select and calculate the contribution of historical samples to the current sample, integrating temporal related information, and overcoming dependence on high‐dimensional local features by minimizing Kullback‐Leibier (KL) divergence penalty terms. Finally, the temporal features are adaptively weighted and connected to a fully connected network to achieve quality prediction. Simulation experiments were conducted in cases of debutanizer and industrial polyethylene production to verify the effectiveness of the proposed method. The experimental results show that compared to the stacked autoencoder (SAE), target dependent stacked autoencoder (TSAE), and stacked isomorphic autoencoder (SIAE) models, the proposed method ADFNN has improved prediction accuracy by 2.4%, 1.7%, and 0.5% in the case of a debutanizer, respectively. In the industrial polyethylene production case, it has increased by 3.6%, 3.3%, and 1.8%, respectively.

A Bagging-SVM field-road trajectory classification model based on feature enhancement

Field-road trajectory classification, in which the semantic labels of unknown trajectory points are predicted by learning the features of agricultural machinery trajectories, has recently received considerable attention in agriculture. At present, most agricultural machinery trajectory samples have imbalanced data distribution problems, and existing field-road trajectory classification methods, which usually use a small set of trajectory features, cannot fully mine the potential information of trajectories, resulting in a low classification accuracy. To address these problems, this paper proposes a Bagging-SVM field-road trajectory classification model based on feature enhancement. First, we use oversampling and undersampling methods to obtain balanced agricultural machinery trajectory data. Second, local features and global features are defined to perform in-depth mining of the spatiotemporal information. Specifically, the local features are derived by calculating the attributes of the dataset, and then the global features are calculated from the local features using the statistical magnitude. Next, principal component analysis (PCA) is employed to identify significant features and decrease the dimensionality. Finally, we introduce the bagging integrated learning method in support vector machines to construct Bagging-SVM as a classifier for field-road trajectory classification. To verify the effectiveness of the proposed method, we conduct experiments using 20 trajectory samples with high-frequency sampling frequencies and 20 trajectory samples with low-frequency sampling frequencies, and the classification F1 score of this method reach 97.01% and 98.71% on two trajectory dataset, respectively. The experimental results show that our method outperforms existing field-road trajectory classification methods.

STUDI KOMPARASI SISTEM PENDIDIKAN DI NORWEGIA DAN AMERIKA SERIKAT

This article was written with the aim of conducting a comparative study of the education systems in Norway and the United States, by conducting an in-depth study of the education systems in each country, then analyzing the similarities and differences in the education systems of the two countries. To carry out data and information mining in this research, the author uses a literature study in which the literature is taken in accordance with the subject matter and analyzed in depth so that conclusions and findings can be drawn in the research. From the results of this research, the author concludes that: (1) The similarities between the education system in Norway and the education system in the United States, both countries have a diverse and inclusive approach and emphasize the development of social and critical skills;(2) The difference between the education system in Norway and the education system in the United States, in Norway it emphasizes more flexibility and holisticness, while the United States is more structured and follows standards. As for the aspect of the teaching approach, Norway focuses more on active learning, while the United States has a combination of traditional and innovative approaches.

Multi-layer and multi-source features stacking ensemble learning for user profile

Electricity bill-sensitive user profiling in the power industry is gradually being recognized as a research hotspot. A multi-layer feature construction method has been proposed, separating the mining of textual and numerical information, addressing the insufficient exploration of textual data in existing user profile processing methods. The complexity of electricity user profiles is addressed through the introduction of a two-stage predictive model based on Stacking ensemble learning. In the first stage, user sensitivity is predicted by utilizing the advantages of MLP (Multi-Layer Perceptron), CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory), and XGB (Extreme Gradient Boosting) in global, local, and missing value handling, respectively. In the second stage, electricity-sensitive users are identified by employing RF (Random Forest). The experimental results show that the MLS-SEL user profile model is higher than the models MVEM, SG and SMUPM in terms of both F1 value and accuracy rate. It is implied that users who could be more sensitive to fluctuations in electricity costs have been identified more accurately.

Water level prediction model based on blockchain and LSTM

Aiming at the shortcomings of traditional water level prediction methods such as insufficient information mining ability and unclear mechanism of heuristic algorithms, this paper proposes for the first time a water level prediction method based on blockchain technology fused with long short-term memory (LSTM) network. The method utilizes blockchain and LSTM neural network to build a combined model, and directly uploads monitoring data such as import and export water flow and water level to predict the water level, which avoids the secondary error brought by the indirect calculation of flow. In this paper, the flow compensation strategy is proposed for the first time, and the monitoring data with large deviations are compensated accordingly to reduce the prediction error from the source. The results show that the combined Blockchain-LSTM model has the smallest prediction error after adopting the compensation strategy, with the MAE of 0.290 and the RMSE of 0.490, which are smaller than those of other models, and has high prediction accuracy and practicability, which provides technical support for real-time scheduling of the South-to-North Water Diversion Reservoir.

A recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information mining

In an e-commerce platform recommendation system, accurately mining users’ preferences and hidden information has always been an important initiative. In this study, based on the potential preferences and hidden characteristics of users, we propose a recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information mining. First, considering the advantages of the VGAE (variational Graph Auto-Encoder) in reconstructing data samples, we propose an EI-VGAE (Explicit Information-Variational Graph Auto-Encoder) to compensate for the lack of explicit feedback. Second, to address the problem that hidden information in e-commerce platforms is difficult to mine, an xDeepFM (eXtremely Deep Factorization Machine) is used to jointly learn higher-order interaction features explicitly and implicitly. A time decay function is also introduced to reduce the impact of higher-order interaction features on the representation of hidden information. Finally, considering the advantages of the attention mechanism for obtaining implicit relational attention and the quantification of the user’s degree of preference in local representation, a GAT (Graph Attention Network)-based recommendation model for the e-commerce platform EI-GAT (Explicit and Implicit Graph Attention Network) is proposed. Our experiments show that the proposed method effectively alleviates the problem of low recommendation accuracy owing to insufficient explicit feedback from consumers and further improves recommendation accuracy by quantifying user preferences.

Two-Stream spectral-spatial convolutional capsule network for Hyperspectral image classification

Recently, the capsule network and its enhanced version named convolutional capsule network (Conv-CapsNet) were applied to hyperspectral image (HSI) classification and delivered outstanding performance, attributing to the enriched capabilities of capsules for feature expression and complex spatial knowledge excavation. However, restricted by the locality of convolutions in primary feature extraction, the rich spectral-spatial diagnostic information found in HSIs were insufficiently utilized, which degrades classification performance to a certain degree. In addition, most capsule networks involve a complex structure with many parameters, which are haunted by a common bottleneck of limited available training samples. To tackle these challenges, this paper presents a novel two-stream spectral-spatial convolutional capsule network (TSCCN) for HSIs to boost accurate classification with small samples. Based on the advanced convolutional capsule concept, a two-stream Conv-CapsNet architecture is developed that reduces trainable parameters and mitigates overfitting when samples are insufficient. Specifically, a 1D Conv-CapsNet and a 2D Conv-CapsNet sharing similar structures are developed as accompanying streams to fully excavate favorable features from the continuous spectra and raw spectral-spatial cubes, with the separate features fused to generate joint deep features for classification. To overcome the locality of primary convolutions, an efficient structural information mining module (SIM) is designed for each stream, to learn complementary cross-channel and cross-kernel inter-dependencies for subsequent capsule learning and further improve classification performance. Experiments on three well-known HSI datasets demonstrate the superiority of the proposed algorithm over nine representative state-of-the-art deep classifiers and its efficacy under small sample conditions, where all the methods were implemented in the Tensor-Flow framework and repeated for 10 times.

Semantic information extraction and search of mineral exploration data using text mining and deep learning methods

Large-scale mineral resource reports offer a wealth of information for geological knowledge mining and mineral explorers’ knowledge discovery. The geological conditions in which mineral deposits develop may be learned a great deal from these mineral exploration reports. Mineral exploration data may be queried and aggregated to effectively mitigate future exploration risks and reduce costs. However, due to the reports being presented in unstructured textual format, it becomes challenging to extract valuable geological data without manually scanning through a vast number of reports. This laborious process poses difficulties for geologists. To address this issue, this paper proposes a system that extracts a set of geologically relevant keywords/keyphrases of each chapter from each mineral exploration reposts using latent Dirichlet allocation (LDA), develops a topic graph, recognizes the geological entity and related relations for constructing a knowledge graph, and uses visualization of those graphs (e.g., topic graphs and knowledge graphs) to explore the contents of the report. The text mining and machine learning technique described here serves as the foundation for future research into incorporating semantic analysis into geological information extraction. The findings of this study show how automated text analysis may help with the quick processing of huge quantities of reports in order to identify target mineral systems and their related geological location and rock mineral composition. The suggested approaches can quickly and reliably convert mineral exploration data (e.g., text, figure, and table) into a structured form, which is a hitherto untouched field in geological knowledge mining.

Attention-Driven Memory Network for Online Visual Tracking.

A memory mechanism has attracted growing popularity in tracking tasks due to the ability of learning long-term-dependent information. However, it is very challenging for existing memory modules to provide the intrinsic attribute information of the target to the tracker in complex scenes. In this article, by considering the biological visual memory mechanisms, we propose the novel online tracking method via an attention-driven memory network, which can mine discriminative memory information and enhance the robustness and reliability of the tracker. First, to reinforce effectiveness of memory content, we design a novel attention-driven memory network. In the network, the long memory module gains property-level memory information by focusing on the state of the target at both the channel and spatial levels. Meanwhile, in reciprocity, we add a short-term memory module to maintain good adaptability when confronting drastic deformation of the target. The attention-driven memory network can adaptively adjust the contribution of short-term and long-term memories to tracking results under the weighted gradient harmonized loss. On this basis, to avoid model performance degradation, an online memory updater (MU) is further proposed. It is designed to mining for target information in tracking results through the Mixer layer and the online head network together. By evaluating the confidence of the tracking results, the memory updater can accurately judge the time of updating the model, which guarantees the effectiveness of online memory updates. Finally, the proposed method performs favorably and has been extensively validated on several benchmark datasets, including object tracking benchmark-50/100 (OTB-50/100), temple color-128 (TC-128), unmanned aerial vehicles-123 (UAV-123), generic object tracking -10k (GOT-10k), visual object tracking-2016 (VOT-2016), and VOT-2018 against several advanced methods.

A unified framework of community hiding using symmetric nonnegative matrix factorization

Community detection can help us to deeply understand community structures and reveal potential social relationships among members. However, as people's concerns over the excessive mining of personal information have grown, the idea of community hiding has been proposed for privacy protection. Most existing community hiding methods based on heuristic approaches and genetic algorithms ignore the generative process of the network and cannot provide a better explanation of the rationality of the hiding mechanism. In addition, they are not simultaneously applicable to the global community, target community, or target nodes. In this study, we address the community hiding problem on three scales: global community hiding (macroscopic), target community hiding (mesoscopic), and target node hiding (microscopic). We propose a unified community hiding framework (CH-SNMF) that can be applied to all three scales. The basic idea is to use symmetric nonnegative matrix factorization to describe the network generative mechanism. It has a potential clustering capability that can mine key links and link sets during the network generation process. Therefore, they can be used to disrupt community structures with minimal perturbation budgets. The experimental results show that CH-SNMF outperforms many advanced baseline methods and effectively protects organizational and individual privacy.