Features Of Dataset Research Articles

Learning Discriminative Features Using ANN-based Progressive Learning Model for Efficient Big Data Classification

Progressive techniques encompass iterative and adaptive approaches that incrementally enhance and optimize data processing by iteratively modifying the analysis process, resulting in improved efficiency and precision of outcomes. These techniques contain a range of components, such as data sampling, feature selection, and learning algorithms. This study proposes the integration of an Artificial Neural Network (ANN) with a Progressive Learning Model (PLM) to enhance the efficacy of learning from large-scale datasets. The SMOTE and Pearson Correlation Coefficient (PCC) methods are commonly employed in imbalanced dataset handling and feature selection. The utilization of progressive weight updating is a notable strategy for improving performance optimization in neural network models. This approach involves the incremental modification of the network’s progressive weights during the training phase rather than relying on gradient values. The proposed method gradually obtains the localization of discriminative data by incorporating information from local details into the overall global structure, effectively reducing the training time by iteratively updating the weights. The model has been examined using two distinct datasets: the Poker hand and the Higgs. The performance of the suggested method is compared with that of classification algorithms: Population and Global Search Improved Squirrel Search Algorithm (PGS-ISSA) and Adaptive E-Bat (AEB). The convergence of Poker’s is achieved after 50 epochs with ANN-PLM; however, without PLM, it takes 65 epochs. Similarly, with the Higgs, convergence is achieved after 25 epochs with PLM and 40 without PLM.

Evaluating soil freezing characteristic curve models for predicting unfrozen water content in freezing soils

Frozen soil is widely distributed around the world, and the phase change and migration of ice and water contribute to various engineering and agricultural challenges. The Soil Freezing Characteristic Curve (SFCC), which elucidates the relationship between water and temperature in permafrost, serves as a crucial tool for studying permafrost and forms the foundational equation for coupled numerical simulations involving permafrost water-heat-force. Different choices of SFCC result in variations in the predicted values of unfrozen water, thereby affecting the accuracy of other issues or numerical simulations. Moreover, there is a lack of studies on the performance of SFCC models under diverse conditions. Therefore, we review and classify some existing SFCC models, selects 14 SFCC models according to functional form, derivation process, physical significance etc., and evaluates these models through published data and statistical indicator RMSE, Radj2 and BIC. The effects of functional form, soil type, cooling stage and initial water content on each model were analyzed using cluster analysis and other means. It is concluded that the SFCC model is affected by the function form, and the power and exponential functions need to be modified by more parameters. The nested soil water characteristic curve model also needs to be modified for the low water content section. Meanwhile, each model also shows sensitivity to the characteristics of datasets, and the performance under different conditions is not the same. Some models may not fit data features under certain conditions. Therefore, it is necessary to reasonably select the SFCC model in different use backgrounds and conditions.

Power Forecasting for Photovoltaic Microgrid Based on MultiScale CNN-LSTM Network Models

Photovoltaic (PV) microgrids comprise a multitude of small PV power stations distributed across a specific geographical area in a decentralized manner. Computational services for forecasting the output power of power stations are crucial for optimizing resource deployment. This paper proposes a deep-learning-based architecture for short-term prediction of PV power. Firstly, in order to make full use of the spatial information between different power stations, a spatio–temporal feature fusion method is proposed. This method is capable of exploiting both the power information of neighboring power stations with strong correlations and meteorological information with the PV feature data of the target power station. By using a multiscale convolutional neural network–long short-term memory (CNN-LSTM) network model, it is capable of generating a PV feature dataset containing spatio–temporal attributes that expand the data source and enhance the feature constraints. It is capable of predicting the output power sequences of power stations in PV microgrids with high model generalization and responsiveness. To validate the effectiveness of the proposed framework, an extensive numerical analysis is also conducted based on a real-world PV dataset.

The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review.

This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer.

Integration of Price Data in Wind Power Sales Systems Using Association Rule Mining Combined with Deep Learning Algorithms

With the growing attention of the global population towards renewable energy sources such as wind power derived from natural resources such as Solar, Geothermal, and Wind energy, there is a growing need for genuine estimation of wind energy. Wind energy is important in the supply of electricity in the global energy markets because of its enormous importance in the delivery of renewable energy. As such, it is crucial for accurate appreciation of the power of the wind to ably respond to issues that relate to the trading of power while at the same time addressing issues to do with planning, scheduling and strategic positioning of wind power generation. Therefore, the present work aims to develop a new model known as the Association Rule with DL-based Wind Power Generation and Price Prediction (ARDL-WPGPP). It utilizes two datasets: An Energy dataset includes various columns like tie, wind onshore forecast, and price actual, whereas the weather features dataset includes only wind speed. These datasets are combined to create a dataset where each transaction represents 2 hours of wind generation. A data mining approach is employed to uncover hidden patterns, rules, concepts, and correlations within these datasets, operating on various types of data including quantitative, textual, and multimedia formats. To efficiently extract rules from the dataset, an improved Apriori algorithm is introduced. Subsequently, the generated rules incorporating wind speed are passed into an improved LSTM model, which learns by comparing the label data. The price actual value serves as the label data, assigning labels to data points based on their actual price value. High price actual values indicate high wind power prices, while low values indicate low wind power prices.

CFTIlandslides, Italian database of historical earthquake-induced landslides

Knowing the location, the extent and the characteristics of any earthquake-induced environmental phenomenon is becoming an increasingly pressing need for civil protection agencies and local administrations. In particular, earthquake-triggered landslides are known for being among the most important sources of secondary hazard, as they may cause significant losses and may delay rescue operations across large areas. The combination of the relatively frequent seismic release with a very high landslide susceptibility makes the Italian territory especially prone to the occurrence of earthquake-induced landslides. The CFTIlandslides dataset features over 1,000 landslides triggered by historical Italian earthquakes (up to 1997). The landslides effects are subdivided into classes based on location accuracy and type of movement. Knowing the distribution of the past earthquake-induced landslides provides the input information for assessing the related hazard. This dataset is addressed to a large audience of potential users, including researchers and scholars, administrators and technicians belonging to local institutions, and civil protection authorities.

Network Intrusion Detection Based on Deep Belief Network Broad Equalization Learning System

Network intrusion detection systems are an important defense technology to guarantee information security and protect a network from attacks. In recent years, the broad learning system has attracted much attention and has been introduced into intrusion detection systems with some success. However, since the traditional broad learning system is a simple linear structure, when dealing with imbalanced datasets, it often ignores the feature learning of minority class samples, leading to a poorer recognition rate of minority class samples. Secondly, the high dimensionality and redundant features in intrusion detection datasets also seriously affect the training time and detection performance of the traditional broad learning system. To address the above problems, we propose a deep belief network broad equalization learning system. The model fully learns the large-scale high-dimensional dataset via a deep belief network and represents it as an optimal low-dimensional dataset, and then introduces the equalization loss v2 reweighing idea into the broad learning system and learns to classify the low-dimensional dataset via a broad equalization learning system. The model was experimentally tested using the CICIDS2017 dataset and fully validated using the CICIDS2018 dataset. Compared with other algorithms in the same field, the model shortens the training time and has a high detection rate and a low false alarm rate.

A Comparative Study on the Impact of Feature Selection and Dataset Resampling on the Performance of the K-Nearest Neighbors (KNN) Classification Algorithm

A Comparative Study on the Impact of Feature Selection and Dataset Resampling on the Performance of the K-Nearest Neighbors (KNN) Classification Algorithm

Bayesian regression modeling and inference of energy efficiency data: the effect of collinearity and sensitivity analysis

The majority of research predicted heating demand using linear regression models, but they did not give current building features enough context. Model problems such as Multicollinearity need to be checked and appropriate features must be chosen based on their significance to produce accurate load predictions and inferences. Numerous building energy efficiency features correlate with each other and with heating load in the energy efficiency dataset. The standard Ordinary Least Square regression has a problem when the dataset shows Multicollinearity. Bayesian supervised machine learning is a popular method for parameter estimation and inference when frequentist statistical assumptions fail. The prediction of the heating load as the energy efficiency output with Bayesian inference in multiple regression with a collinearity problem needs careful data analysis. The parameter estimates and hypothesis tests were significantly impacted by the Multicollinearity problem that occurred among the features in the building energy efficiency dataset. This study demonstrated several shrinkage and informative priors on likelihood in the Bayesian framework as alternative solutions or remedies to reduce the collinearity problem in multiple regression analysis. This manuscript tried to model the standard Ordinary Least Square regression and four distinct Bayesian regression models with several prior distributions using the Hamiltonian Monte Carlo algorithm in Bayesian Regression Modeling using Stan and the package used to fit linear models. Several model comparison and assessment methods were used to select the best-fit regression model for the dataset. The Bayesian regression model with weakly informative prior is the best-fitted model compared to the standard Ordinary Least Squares regression and other Bayesian regression models with shrinkage priors for collinear energy efficiency data. The numerical findings of collinearity were checked using variance inflation factor, estimates of regression coefficient and standard errors, and sensitivity of priors and likelihoods. It is suggested that applied research in science, engineering, agriculture, health, and other disciplines needs to check the Multicollinearity effect for regression modeling for better estimation and inference.

ASD-GANNet: A Generative Adversarial Network-Inspired Deep Learning Approach for the Classification of Autism Brain Disorder.

The classification of a pre-processed fMRI dataset using functional connectivity (FC)-based features is considered a challenging task because of the set of high-dimensional FC features and the small dataset size. To tackle this specific set of FC high-dimensional features and a small-sized dataset, we propose here a conditional Generative Adversarial Network (cGAN)-based dataset augmenter to first train the cGAN on computed connectivity features of NYU dataset and use the trained cGAN to generate synthetic connectivity features per category. After obtaining a sufficient number of connectivity features per category, a Multi-Head attention mechanism is used as a head for the classification. We name our proposed approach "ASD-GANNet", which is end-to-end and does not require hand-crafted features, as the Multi-Head attention mechanism focuses on the features that are more relevant. Moreover, we compare our results with the six available state-of-the-art techniques from the literature. Our proposed approach results using the "NYU" site as a training set for generating a cGAN-based synthetic dataset are promising. We achieve an overall 10-fold cross-validation-based accuracy of 82%, sensitivity of 82%, and specificity of 81%, outperforming available state-of-the art approaches. A sitewise comparison of our proposed approach also outperforms the available state-of-the-art, as out of the 17 sites, our proposed approach has better results in the 10 sites.

3DGEN: a framework for generating custom-made synthetic 3D datasets for civil structure health monitoring

The availability of high-quality datasets is increasingly critical in the field of computer vision-based civil structural health monitoring, where deep learning approaches have gained prominence. However, the lack of specialized datasets for such tasks poses a significant challenge for training a reliable model. To address this challenge, a framework, 3DGEN, is proposed to swiftly generate realistic synthetic 3D datasets which can be targeted for specific tasks. The framework is based on diverse 3D civil structural models, rendering them from various angles and providing depth information and camera parameters for training neural networks. By employing mathematical methods, such as analytical solutions and/or numerical simulations, deformation of civil engineering structures can be generated, ensuring a reliable representation of their real-world shapes and characteristics in the 3D datasets. For texture generation, a generative 3D texturing method enables users to specify desired textures using plain English sentences. Two successful experiments are conducted to (1) assess the efficiency of generating the 3D datasets using two distinct structures, (2) train a monocular depth estimation network to perform 3D surface reconstruction with the generated dataset. Notably, 3DGEN is not limited to 3D surface reconstruction; it can also be used for training neural networks for various other tasks. The code and dataset are available at: https://github.com/YANDA-SHAO/Beam-Dataset-SE

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

Knowledge Distillation in Image Classification: The Impact of Datasets

As the demand for efficient and lightweight models in image classification grows, knowledge distillation has emerged as a promising technique to transfer expertise from complex teacher models to simpler student models. However, the efficacy of knowledge distillation is intricately linked to the choice of datasets used during training. Datasets are pivotal in shaping a model’s learning process, influencing its ability to generalize and discriminate between diverse patterns. While considerable research has independently explored knowledge distillation and image classification, a comprehensive understanding of how different datasets impact knowledge distillation remains a critical gap. This study systematically investigates the impact of diverse datasets on knowledge distillation in image classification. By varying dataset characteristics such as size, domain specificity, and inherent biases, we aim to unravel the nuanced relationship between datasets and the efficacy of knowledge transfer. Our experiments employ a range of datasets to comprehensively explore their impact on the performance gains achieved through knowledge distillation. This study contributes valuable guidance for researchers and practitioners seeking to optimize image classification models through kno-featured applications. By elucidating the intricate interplay between dataset characteristics and knowledge distillation outcomes, our findings empower the community to make informed decisions when selecting datasets, ultimately advancing the field toward more robust and efficient model development.

Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping.

This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios.

Exploring optimal features and image analysis methods for crop type classification from the perspective of crop landscape heterogeneity

Agricultural landscape structure (e.g., the shape of fields, crop diversity, and landscape heterogeneity) greatly influences the selection of methods for large-scale crop mapping using remote sensing data. However, in-depth assessments of its impacts on crop mapping remain infrequent in the existing literature. This study investigated the optimal crop identification features and image analysis methods including pixel- and object-based approaches on crop classification, through the integration of spectral and textural features across various quantitative agricultural landscapes. In the experiments, crop fields were initially delineated into four distinct landscapes using the K-means clustering algorithm based on analyzing 13 selected landscape metrics such as PLAND, LSI and SHDI. Both pixel- and object-based approaches were then employed to conduct crop classification was then conducted using 48 selected features including 9 band reflectance, 23 vegetation indices (VIs), and 16 textures) and two image analysis methods. Specifically, five classification schemes for the different combinations of feature datasets and image analysis methods were explored to assess the impacts of crop heterogeneity on crop classification. Results indicated the five landscape metrics (e.g., SPLIT, SHEI, Average distance, etc.) performed best in assessing crop heterogeneity. In general, spectral bands and VIs had a higher contribution in the compositional heterogeneity, while textural features and VIs played a more important role in the configurational heterogeneity. VIs in the object-based approach and texture features in the pixel-based approach can improved crop classification accuracy in configurational landscapes. The findings provide a theoretical basis on selecting optimal features and image analysis methods for crop classification in complex agricultural landscapes.

From A-to-Z review of clustering validation indices

Data clustering involves identifying latent similarities within a dataset and organizing them into clusters or groups. The outcomes of various clustering algorithms differ as they are susceptible to the intrinsic characteristics of the original dataset, including noise and dimensionality. The effectiveness of such clustering procedures directly impacts the homogeneity of clusters, underscoring the significance of evaluating algorithmic outcomes. Consequently, the assessment of clustering quality presents a significant and complex endeavor. A pivotal aspect affecting clustering validation is the cluster validity metric, which aids in determining the optimal number of clusters. The main goal of this study is to comprehensively review and explain the mathematical operation of internal and external cluster validity indices, but not all, to categorize these indices and to brainstorm suggestions for future advancement of clustering validation research. In addition, we review and evaluate the performance of internal and external clustering validation indices on the most common clustering algorithms, such as the evolutionary clustering algorithm star (ECA*). Finally, we suggest a classification framework for examining the functionality of both internal and external clustering validation measures regarding their ideal values, user-friendliness, responsiveness to input data, and appropriateness across various fields. This classification aids researchers in selecting the appropriate clustering validation measure to suit their specific requirements.

A fast dual-module hybrid high-dimensional feature selection algorithm

When dealing with large-scale datasets, high-dimensional feature selection plays a crucial role in improving the performance and interpretability of machine learning models. However, it still faces the problems of the “dimensionality curse” and high computational cost in dealing with high-dimensional datasets. In this paper, we develop a dual-module hybrid feature selection algorithm based on correlation filtering and a multi-objective evolutionary algorithm based on dynamic variation distance (HMOFS-CFDVD), which aims to reduce the computational cost of high-dimensional features and search space. Firstly, a coarse-grained feature filtering method based on correlation is proposed, enabling the algorithm to quickly identify potentially better feature subsets in the later stages. After that, a fine-grained feature selection is further implemented based on the multi-objective evolutionary algorithm with sample variation distance to obtain a high-quality feature subset. This stage incorporates a novel population initialization method with self-regulation, an adaptive evolution strategy, an optimal sample selection mechanism based on dynamic sample distance, and an improved diversity mechanism to enhance the evolutionary performance. Moreover, the feature relevance metric is introduced as a third objective to improve the overall algorithm's performance. Experimental results on 12 high-dimensional feature datasets demonstrate that the proposed HMOFS-CFDVD achieves high accuracy and produces a smaller subset of features.

Differentiating Gliosarcoma from Glioblastoma: A Novel Approach Using PEACE and XGBoost to Deal with Datasets with Ultra-High Dimensional Confounders.

In this study, we used a recently developed causal methodology, called Probabilistic Easy Variational Causal Effect (PEACE), to distinguish gliosarcoma (GSM) from glioblastoma (GBM). Our approach uses a causal metric which combines Probabilistic Easy Variational Causal Effect (PEACE) with the XGBoost, or eXtreme Gradient Boosting, algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach uses the complete dataset with PEACE and the XGBoost algorithm. PEACE provides a comprehensive measurement of direct causal effects, applicable to both continuous and discrete variables. Our method provides both positive and negative versions of PEACE together with their averages to calculate the positive and negative causal effects of the radiomic features on the variable representing the type of tumor (GSM or GBM). In our model, PEACE and its variations are equipped with a degree d which varies from 0 to 1 and it reflects the importance of the rarity and frequency of the events. By using PEACE with XGBoost, we achieved a detailed and nuanced understanding of the causal relationships within the dataset features, facilitating accurate differentiation between GSM and GBM. To assess the XGBoost model, we used cross-validation and obtained a mean accuracy of 83% and an average model MSE of 0.130. This performance is notable given the high number of columns and low number of rows (code on GitHub).

An improved mountain gazelle optimizer based on chaotic map and spiral disturbance for medical feature selection.

Feature selection is an important solution for dealing with high-dimensional data in the fields of machine learning and data mining. In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. First, the gazelle population is initialized using iterative chaotic map with infinite collapses (ICMIC) mapping, which increases the diversity of the population. Second, a nonlinear control factor is introduced to balance the exploration and exploitation components of the algorithm. Individuals in the population are perturbed using a spiral perturbation mechanism to enhance the local search capability of the algorithm. Finally, a neighborhood search strategy is used for the optimal individuals to enhance the exploitation and convergence capabilities of the algorithm. The superior ability of the IMGO algorithm to solve continuous problems is demonstrated on 23 benchmark datasets. Then, BIMGO is evaluated on 16 medical datasets of different dimensions and compared with 8 well-known metaheuristic algorithms. The experimental results indicate that BIMGO outperforms the competing algorithms in terms of the fitness value, number of selected features and sensitivity. In addition, the statistical results of the experiments demonstrate the significantly superior ability of BIMGO to select the most effective features in medical datasets.

Image-based intrusion detection system for GPS spoofing cyberattacks in unmanned aerial vehicles

The operations of unmanned aerial vehicles (UAVs) are susceptible to cybersecurity risks, mainly because of their firm reliance on the Global Positioning System (GPS) and radio frequency (RF) sensors. GPS and RF sensors are vulnerable to potential threats, such as spoofing attacks that can cause the UAVs to behave erratically. Since these threats are widespread and potent, it is imperative to develop effective intrusion detection systems. In this paper, we propose an image-based intrusion detection system for detecting GPS spoofing cyberattacks based on a deep learning methodology. We combine convolutional neural networks with Principal Component Analysis (PCA) to reduce the dimensionality of the dataset features, data augmentation to increase the size and diversity of the training dataset, and transfer learning to improve the proposed model’s performance with limited data to design a fast, accurate, and general method. Extensive numerical experiments demonstrate the effectiveness of the proposed solution carried out using benchmark datasets. We achieved an accuracy of 100% within a running time of 120.64 s at 0.3529 ms latency and a detection time of 2.035 s in the case of the training dataset. Further, using this trained model, we achieved an accuracy of 99.25% within a detection time of 2.721 s on an unseen dataset that was unrelated to the one used for training the model. In contrast, other models, such as Inception-v3, showed lower accuracy on unseen datasets. However, Inception-v3 performance improved significantly after Bayesian optimization, with the Tree-structured Parzen Estimator reaching 99.06% accuracy. Our results demonstrate that the proposed image-based intrusion detection method outperforms the existing solutions while providing a general model for detecting cyberattacks included in unseen datasets.