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Analysis of microarray data is a highly challenging problem due to the inherent complexity in the nature of the data associated with higher dimensionality, smaller sample size, imbalanced number of classes, noisy data-structure, and higher variance of feature values. This has led to lesser classification accuracy and over-fitting problem. In this work, the authors aimed to develop a deep feedforward method to classify the given microarray cancer data into a set of classes for subsequent diagnosis purposes. They have used a 7-layer deep neural network architecture having various parameters for each dataset. The small sample size and dimensionality problems are addressed by considering a well-known dimensionality reduction technique namely principal component analysis. The feature values are scaled using the Min–Max approach and the proposed approach is validated on eight standard microarray cancer datasets. To measure the loss, a binary cross-entropy is used and adaptive moment estimation is considered for optimisation. The performance of the proposed approach is evaluated using classification accuracy, precision, recall, f-measure, log-loss, receiver operating characteristic curve, and confusion matrix. A comparative analysis with state-of-the-art methods is carried out and the performance of the proposed approach exhibit better performance than many of the existing methods.

This paper describes the multilayer voting algorithm, a novel autonomous star identification method for spacecraft attitude determination. The proposed algorithm includes two processes: an initial match process and a verification process. In the initial match process, a triangle voting scheme is used to acquire candidates of the detected stars, in which the triangle unit is adopted as the basic voting unit. During the identification process, feature extraction is implemented, and each triangle unit is described by its singular values. Then the singular values are used to search for candidates of the imaged triangle units, which further improve the efficiency and robustness of the algorithm. After the initial match step, a verification method is applied to eliminate incorrect candidates from the initial results and then outputting the final match results of the imaged stars. Experiments show that our algorithm has more robustness to position noise, magnitude noise, and false stars than the other three algorithms, the identification speed of our algorithm is largely faster than the geometric voting algorithm and optimized grid algorithm. However, it takes more memory, and SVD also seems faster.

In this paper, intelligent information retrieval of relevant literary content through collaborative filtering algorithms in Internet technology is used to analyze the similarity between users and, thus, to select similar items in a weighted ranking. The Jacobian formula is used to calculate the common feature values and thus predict the user’s unrated item ratings. Word normalization is performed through information extraction of text keywords to effectively abstract the dimensionality of text features. The results showed that the influence of learning based on language literacy was 0.697, the preference for the literary genre of prose was as high as 49.4%, and students could accept infiltration teaching. It indicates that making full use of Internet technology means can break the time and space limitations in the traditional teaching of literature, provide effective support for students’ interactive communication, expand students’ cultural horizons, and improve their humanistic literacy.

Diabetes, a significant global health crisis, is primarily driven in India by unhealthy diets and sedentary lifestyles, with rapid urbanization amplifying these effects through convenience-oriented living and limited physical activity opportunities, underscoring the need for advanced preventative strategies and technology for effective management. This study integrates Shapley Additive explanations (SHAPs) into ensemble machine learning models to improve the accuracy and efficiency of diabetes predictions. By identifying the most influential features using SHAP, this study examined their role in maintaining high predictive performance while minimizing computational demands. The impact of feature selection on model accuracy was assessed across ten models using three feature sets: all features, the top three influential features, and all except these top three. Models focusing on the top three features achieved superior performance, with the ensemble model attaining a better performance in most of the metrics, outperforming comparable approaches. Notably, excluding these features led to a significant decline in performance, reinforcing their critical influence. These findings validate the effectiveness of targeted feature selection for efficient and robust clinical applications.

Sleeping postures are one of the indicators for judging sleep quality and preventing sudden diseases. The sleeping postures not only affect people’s sleep quality but also has great significance for the diagnosis of apnea syndrome and bedsores. To realize and recognize the laying positions, this paper researches the regularized extreme learning (RELM) algorithm to analyze the pressure due to lying positions. Based on this algorithm first, the array pressure sensor is used to obtain the back lying posture pressure image, and the image is pre-processed to complete the extraction of multiple feature values (Geometric features, Energy features, and Colour features). Second, the multi-feature values are normalized and finally, these multi-feature values are trained and predicted by the RELM algorithm. In concluding this, the accuracy of lying posture recognition was the highest, achieving 98.75 percent, this is when 1120 datasets of feature values were used as training data and 160 sets as test data while the hidden nodes were 80. RELM algorithm can overcome the problems of extreme learning (ELM) algorithm, such as slow learning speed and local minimum value, and so on. Therefore, this method can be applied in the scenarios of lying posture recognition.

The correct prediction of heating network pipeline failure rates can increase the reliability of the heat supply to consumers in the cold season. However, due to the large number of factors affecting the corrosion of underground steel pipelines, it is difficult to achieve high prediction accuracy. The purpose of this study is to identify connections between the failure rate of heating network pipelines and factors not taken into account in traditional methods, such as residual pipeline wall thickness, soil corrosion activity, previous incidents on the pipeline section, flooding (traces of flooding) of the channel, and intersections with communications. To achieve this goal, the following machine learning algorithms were used: random forest, gradient boosting, support vector machines, and artificial neural networks (multilayer perceptron). The data were collected on incidents related to the breakdown of heating network pipelines in the cities of Kazan and Ulyanovsk. Based on these data, four intelligent models have been developed. The accuracy of the models was compared. The best result was obtained for the gradient boosting regression tree, as follows: MSE = 0.00719, MAE = 0.0682, and MAPE = 0.06069. The feature «Previous incidents on the pipeline section» was excluded from the training set as the least significant.

The rapid expansion of Internet of Things (IoT) technologies has highlighted the need for reliable intrusion detection systems (IDSs), yet the majority of existing studies rely on single-dataset evaluations, raising concerns about their real-world generalisation capability. This study addresses this limitation by systematically investigating distributional shift across heterogeneous IoT intrusion detection datasets and their impact on model behaviour. To achieve this, a unified feature space is constructed using BoT-IoT, ToN-IoT, and UNSW-NB15 datasets, followed by a comprehensive preprocessing pipeline including attack class alignment, distribution-preserving sampling for class imbalance, and feature selection based on cross-dataset feature value propagation analysis. Furthermore, feature-specific transformations and correlation-based dimensionality reduction are applied to enhance statistical consistency and model stability. To simulate realistic deployment scenarios, models are trained on combinations of datasets and evaluated on unseen datasets. The results reveal that distributional inconsistencies and dataset-specific feature biases significantly degrade cross-dataset performance, despite strong within-dataset results. The proposed framework provides a systematic understanding of feature-level behaviour across datasets, identifying both stable and bias-prone features. These findings highlight the necessity of distribution-aware preprocessing and feature analysis for developing robust and generalisable IoT intrusion detection systems.

Aiming at the detection challenges caused by the diverse morphology of microcracks in plate heat exchanger sheets, this paper proposes a detection framework that integrates parameter-driven adaptive template generation, binary scale optimization, and feature value threshold segmentation using convolutional networks. First, based on the grayscale characteristics of microcracks, an adaptive template generation model driven by key parameters (width, height, and endpoint grayscale difference) is constructed, obtaining a unique solution by solving the boundary conditions of physical features. Second, to overcome the challenge of microcrack width continuity, a binary scale optimization strategy based on the critical decay ratio k* of the correlation coefficient is designed, enabling the coverage of continuous-width defects with a finite set of templates. Finally, enhanced features are fed into a convolutional network. Utilizing the bimodal characteristic of the feature value distribution, the region corresponding to the extreme values in the top 0.3% before the foreground peak is located using 3σ extreme value statistics, achieving adaptive segmentation to identify defect regions. Evaluation on the self-built microcrack dataset SUT-B1 yielded results of 83.59% recall, 80.55% precision, and an F1 score of 81.98%. This method outperforms small object detection networks, demonstrating its advantage in morphological adaptability for small-sized objects. It also surpasses receptive field optimization modules, proving the necessity of structural optimization. The proposed method demonstrates practicality and scalability in the field of industrial inspection.

The properties of solvability/regularity of problems and correctness/completeness of algorithmic models are fundamental components of the algebraic approach to pattern recognition. In this paper, we formulate the principles of the metric approach to the data analysis of poorly formalized problems and hence with obtain metric forms of the criteria of solvability, regularity, correctness, and completeness. In particular, the analysis of the compactness properties of metric configurations allowed us to obtain a set of sufficient conditions for the existence of correct algorithms. These conditions can be used for assessment of the quality of the methods of formalization of the problems for arbitrary algorithms and algorithmic models. The general schema proposed for the data analysis of poorly formalized problems includes the criteria in the cross-validation form and can assess not only the quality of formalization, but also the extent of overtraining pertaining to the procedures of generation and selection of feature descriptions.

Accurate identification of water-inrush sources is critical for deep mining safety. This study proposes an entropy-weighted variable fuzzy set (EW-VFS) model, which uses information entropy to objectively determine the importance of different hydrochemical indicators, to discriminate complex mixed water sources in the Sunzhuang Minefield, North China. In this study, we analyzed 86 water samples from five key aquifers—Permian sandstone fractured aquifers, Ordovician limestone karst aquifers, and three thin-layer limestone aquifers—using nine hydrochemical parameters. Entropy weight analysis identified Mg²+ and HCO3− as the dominant indicators for source discrimination. The EW-VFS model achieved an overall accuracy of 83.33%, demonstrating high reliability, particularly for Permian sandstone fractured water and Ordovician limestone water. Furthermore, a time-series analysis of the model's rank feature value (Hi) revealed a dynamic evolution of the inrush source, showing a clear transition between different thin-layer limestone aquifers during mining operations. This study's findings demonstrate the model's utility in identifying multisource water inrush. However, its performance in differentiating the highly similar thin-layer limestone aquifers shows potential for further enhancement, which could be addressed with more comprehensive hydrogeochemical data.