Wyniki wyszukiwania - BazTech

1

Optimizing Built-Up Area Extraction in Semi-Arid Regions Using Sentinel-2A Imagery: A Comparative Analysis of Spectral Indices and PCA-Based Classification in Batna, Algeria

Touati Wahiba, Kalla Mahdi, Kacha Lemya

Geomatics and Environmental Engineering

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2026

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Vol. 20, no. 3

25--49

EN

Accurate detection of built-up areas in semi-arid regions is vital for urban planning and environmental monitoring. However, built-up surfaces and bare soils often produce very similar spectral responses. As a result, this similarity causes confusion in satellite image classification. Additionally, spectral overlap among urban materials, bare soil, and sparse vegetation further complicates detection. This study evaluates several spectral indices, including DBSI, NDTI, NDVI, BRBA, and BSI, combined with Principal Component Analysis (PCA) to enhance built-up area extraction from Sentinel-2A imagery. Images captured during the driest season were selected to maximize spectral contrast. Three classification schemes based on Support Vector Machine (SVM) were tested. The first scheme used DBSI, NDTI, and NDVI. The second used BRBA, NDTI, and NDVI. The third relied on PCA-derived components. The results indicate that the PCA-based approach achieved the highest classification accuracy at 95%. In comparison, the DBSI/NDTI/NDVI combination reached 93%, while the BRBA/NDTI/NDVI scheme achieved 92%. Therefore, PCA helps reduce spectral confusion and enhances the identification of built-up areas in semi-arid environments. Overall, combining multiple spectral indices with dimensionality reduction offers a reliable method for urban analysis using Sentinel-2 imagery.

2

Singular value versus eigenvalue decomposition efficiency in computing principal components for dimensionality reduction of large datasets

Romanuke Vadim

Scientific Journal of Gdynia Maritime University

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2025

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nr 134

56--70

EN

Principal component analysis, being one of the best techniques for dimensionality reduction, is implemented by using one of the two high-accuracy algorithms: the singular value decomposition (SVD) and eigenvalue decomposition (EVD). The EVD is generally faster than the SVD, except for datasets with fewer observations or when the observation has fewer features. Apart from cases of shallower datasets consisting of just a few hundred double-precision observations, the EVD speeds up computing principal components by at least 4.5%, whereas the average speedup in 45% widely varies from 12% to 92%. The speedup on non-shallower single-precision datasets is roughly similar, but it nonetheless makes no sense due to EVD poor accuracy while operating on numeric data with single precision. The EVD is efficient if the dataset consists of no fewer than a few hundred observations (objects) having at least three double-precision features.

3

Mining method selection based on hierarchical clustering and correspondence analysis

Kaykov Dimitar, Mijalkovski Stojance, Arsova-Borisova Kremena

Mining Science

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2025

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Vol. 32

105--117

EN

Selecting an optimal mining method is a complex and critical decision in underground mining, influenced by multiple geological, technical, and economic parameters. This study introduces a novel frame-work that combines Hierarchical Clustering (HC) and Correspondence Analysis (CA) to enhance the selection process by evaluating the consistency and similarity among outcomes from both first-pass methods (UBC and Nicholas) and several multi-criteria decision-making (MCDM) techniques (including AHP, EDAS, PROMETHEE II, AHP-PROMETHEE, TOPSIS, and VIKOR). The proposed HC-CA approach identifies consistent conflicts among the considered mining methods and quantifies the agreement among the initial assumptions of the adopted selection procedures. A case study of a Pb-Zn deposit demonstrates that the framework can effectively detect consistent and co-occurring (i.e., conflicting) solutions, such as Cut-and-Fill Stoping, Shrinkage Stoping, and Sublevel Stoping. The results show that the adopted design criteria align more closely with the UBC selection method, compared to the Nicholas selection procedure for the considered deposit. Additionally, applying the HC-CA approach to the input matrices prior to applying the MCDM methods can yield different results, compared to subjecting the MCDM output scores to the proposed framework. This integrative approach extends traditional selection procedures and links them with commonly used MCDM methodologies and unsupervised machine learning methods by enabling flexible strategy development, with the inclusion of considering mixed-mining-method scenarios tailored to the deposit. Additionally, the approach offers improved decision support in early project stages by visualizing affinities among different assumptions and hence potentially mitigating biases during the following design stage.

4

Comparative features reduction investigation for Android malware detection on Boolean data

Irawan Ary, Bilski Piotr, Gwardys Grzegorz

International Journal of Electronics and Telecommunications

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2025

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Vol. 71, No. 4

art. no. 35, s. 1-7

EN

The aim of this research is to enhance the effectiveness of Android malware detection systems by implementing dimensionality reduction techniques on Boolean data. Algorithms such as Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and Multi-Correspondence Analysis (MCA) serve as operations preceding the classification stage. The analysis is carried out using multiple classifiers such as Random Forest Classifier, Logistic Regression, and Support Vector Machines to measure how effective they can detect cyber threats. Results show that the Decision Tree Classifier, implemented without dimensionality reduction, achieved the optimal results with 100% accuracy. Efficient feature selection and rapid computation in the context of malware detection are necessary for real-time mobile cyber environment applications.

5

Probabilistic lane segmentation using a low-dimensional linear parametrization

Acuña Carlos, Arechavaleta Gustavo, Castelán Mario

International Journal of Applied Mathematics and Computer Science

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2025

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Vol. 35, no. 1

179--189

EN

Lane detection is an important module for active safety systems since it increases safety and reduces traffic accidents caused by driver inattention. Illumination changes or occlusions make lane detection a challenging task, especially if the detection is performed from a single image. Consequently, this paper presents a probabilistic approach based on the Kalman filter, which uses information from previous image frames to estimate the lane that could not be detected in the current image frame, considering uncertainty in the prediction as well as in the detection. To this end, a principal component analysis of the segmented curvature is introduced with the purpose of dimensionality reduction, moving from a large dimensional pixel representation to a considerably reduced space representation. Furthermore, the proposed approach is compared with a fully connected pretrained CNN model for lane detection, demonstrating that the proposed method has a lower computational cost in addition to a smoother transition between lane estimates.

6

SAE-AM: Enhancing Network Intrusion Detection Using Sparse Autoencoders with Attention Modules

Pamena Akanksha, Naik S Manohar

Applied Cybersecurity & Internet Governance

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2025

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Vol. 4, No. 1

art. no. 9., s. 1-38

EN

In response to the relentless evolution of cyber threats that continue to outpace traditional defence mechanisms, this study addresses key limitations in existing Intrusion Detection Systems (IDS), particularly those related to high dimensionality and computational inefficiency. We propose a novel framework, sparse autoencoders with attention modules (SAE-AM), which integrates SAE with both channel and positional attention mechanisms to enhance feature representation and optimise resource utilisation. While SAE effectively perform dimensionality reduction, the attention modules capture global dependencies across diverse input features. Leveraging a deep learning model, specifically a multi-layer perceptron classifier – our framework efficiently classifies normal and attack samples. Extensive evaluations on benchmark network intrusion datasets, CICIDS2017, NSL-KDD, and UNSW-NB15, demonstrate the robustness and superior performance of the proposed method, achieving 99% accuracy and minimal false alarm rates across all datasets. SAE-AM makes significant strides in overcoming the core challenges of NIDS by reducing dimensionality and improving computational efficiency. This novel approach contributes to enhanced network security, offering a lightweight and effective solution for real-world intrusion detection scenarios.

7

An Age-Group Ranking Model for Facial Age Estimation

Akinyemi Joseph D., Onifade Olufade F. W.

Machine Graphics and Vision

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2024

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Vol. 33, No. 1

21--45

EN

Age prediction has become an important Computer Vision task. Although this task requires the age of an individual to be predicted from a given face, research has shown that it is more intuitive and easier for humans to decide which of two individuals is older than to decide how old an individual is. This work follows this intuition to aid the age prediction of a face by exploiting the age information available from other faces. It goes further to explore the statistical relationships between facial features within age groups to compute age-group ranks for a given face. The resulting age-group rank is low-dimensional and age-discriminatory, thus improving age prediction accuracy when fed into an age predictor. Experiments on publicly available facial ageing datasets (FGnet, PAL, and Adience) reveal the effectiveness of the proposed age-group ranking model when used with traditional Machine learning algorithms as well as Deep Learning algorithms. Cross-dataset validation, a method of training and testing on entirely different datasets, was also employed to further investigate the effectiveness of this method.

8

Using reinforcement learning to select an optimal feature set

Akhiat Yassine, Zinedine Ahmed, Chahhou Mohamed

Journal of Automation Mobile Robotics and Intelligent Systems

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2024

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Vol. 18, No. 1

56--66

EN

Feature Selection (FS) is an essential research topic in the area of machine learning. FS, which is the process of identifying the relevant features and removing the irrelevant and redundant ones, is meant to deal with the high dimensionality problem for the sake of selecting the best performing feature subset. In the literature, many feature selection techniques approach the task as a research problem, where each state in the search space is a possible feature subset. In this paper, we introduce a new feature selection method based on reinforcement learning. First, decision tree branches are used to traverse the search space. Second, a transition similarity measure is proposed so as to ensure exploit-explore trade-off. Finally, the informative features are the most involved ones in constructing the best branches. The performance of the proposed approaches is evaluated on nine standard benchmark datasets. The results using the AUC score show the effectiveness of the proposed system.

9

Dimensionality reduction of rotor fault dataset based on joint embedding of multi-class graphs

Zhang Yongfei, Wang Jiaxuan, Zhao Rongzhen, Deng Linfeng

Eksploatacja i Niezawodność

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2024

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Vol. 26, no. 1

art. no. 177417

EN

Traditional dimensionality reduction techniques usually rely on a single or a limited number of similar graphs for graph embedding, which limits their ability to extract more information about the internal structure of the data. To address this problem, this study proposes a rotor fault dataset dimensionality reduction algorithm based on multi-class graph joint embedding (MCGJE). The algorithm first overcomes the defect that the traditional feature space cannot take both local and global information into account by constructing local and global median feature line graphs; secondly, based on the graph embedding framework, the algorithm also constructs a hypergraph structure for inscribing complex multivariate relationships between high-dimensional data in the feature space, which in turn enables it to contain more fault information. Finally, we conducted two different rotor fault simulation experiments. The results show that the MCGJE-based algorithm has robustdimensionality reduction capability and can significantly improve the accuracy of fault identification.

10

A contemporary multi-objective feature selection model for depression detection using a hybrid pBGSK optimization algorithm

Kavi Priya Santhosam, Pon Karthika Kasirajan

International Journal of Applied Mathematics and Computer Science

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2023

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Vol. 33, no. 1

117--131

EN

Depression is one of the primary causes of global mental illnesses and an underlying reason for suicide. The user generated text content available in social media forums offers an opportunity to build automatic and reliable depression detection models. The core objective of this work is to select an optimal set of features that may help in classifying depressive contents posted on social media. To this end, a novel multi-objective feature selection technique (EFS-pBGSK) and machine learning algorithms are employed to train the proposed model. The novel feature selection technique incorporates a binary gaining-sharing knowledge-based optimization algorithm with population reduction (pBGSK) to obtain the optimized features from the original feature space. The extensive feature selector (EFS) is used to filter out the excessive features based on their ranking. Two text depression datasets collected from Twitter and Reddit forums are used for the evaluation of the proposed feature selection model. The experimentation is carried out using naive Bayes (NB) and support vector machine (SVM) classifiers for five different feature subset sizes (10, 50, 100, 300 and 500). The experimental outcome indicates that the proposed model can achieve superior performance scores. The top results are obtained using the SVM classifier for the SDD dataset with 0.962 accuracy, 0.929 F1 score, 0.0809 log-loss and 0.0717 mean absolute error (MAE). As a result, the optimal combination of features selected by the proposed hybrid model significantly improves the performance of the depression detection system.

11

Research on communication emitter identification based on semi-supervised dimensionality reduction in complex electromagnetic environment

Ge Wei, Qi Lin, Tong Lin, Zhu Jun, Zhang Jing, Zhao Dongyang, Li Ke

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2023

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Vol. 71, nr 4

art. no. e145766

EN

The individual identification of communication emitters is a process of identifying different emitters based on the radio frequency fingerprint features extracted from the received signals. Due to the inherent non-linearity of the emitter power amplifier, the fingerprints provide distinguishing features for emitter identification. In this study, approximate entropy is introduced into variational mode decomposition, whose features performed in each mode which is decomposed from the reconstructed signal are extracted while the local minimum removal method is used to filter out the noise mode to improve SNR. We proposed a semi-supervised dimensionality reduction method named exponential semi-supervised discriminant analysis in order to reduce the high-dimensional feature vectors of the signals, and LightGBM is applied to build a classifier for communication emitter identification. The experimental results show that the method performs better than the state-of-the-art individual communication emitter identification technology for the steady signal data set of radio stations with the same plant, batch and model.

12

XOR-based decomposition and its application in memory-based and reversible logic synthesis

Mazurkiewicz Tomasz

Annals of Computer Science and Information Systems

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2023

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Vol. 35

183--190

EN

In this research paper, we propose a novel approach to digital circuit design using XOR-based decomposition. The proposed technique utilizes XOR gates as a fundamental building block for decomposing complex Boolean functions into simpler forms, leading to more efficient and compact digital circuits. We demonstrate the effectiveness of our approach in two different contexts: memory-based logic synthesis and reversible logic synthesis. In particular, we demonstrate that the proposed technique can efficiently reduce the number of input variables, which is a crucial task when using memories in the design. Obtained results prove that the XOR-based approach can efficiently complement variable reduction and dimensionality reduction algorithms. Furthermore, we show its application in generating the XOR-AND-XOR form of a reversible function and demonstrate how to combine it with another technique, i.e., a functional decomposition for reversible logic synthesis

13

Efficient multi-classifier wrapper feature-selection model. Application for dimension reduction in credit scoring

Bouaguel Waad

Computer Science

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2022

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T. 23 (1)

133--155

EN

The task of identifying the most relevant features for a credit-scoring application is a challenging task. Reducing the number of redundant and unwanted features is an inevitable task for improving the performance of a credit-scoring model. The wrapper approach is usually used in credit-scoring applications to identify the most relevant features. However, this approach suffers from the issue of subset generation and the use of a single classifier as an evaluation function. The problem here is that each classifier may give different results that can be interpreted differently. Hence, we propose an ensemble wrapper featureselection model in this study that is based on a multi-classifier combination. In the first stage, we address the problem of subset generation by minimizing the search space through a customized heuristic. Then, a multi-classifier wrapper evaluation is applied using two-classifier-arrangement approaches in order to select a set of mutually approved sets of relevant features. The proposed method was evaluated on four credit datasets and has shown good performance as compared to individual classifier results.

14

Considering various aspects of models’ quality in the ML pipeline - application in the logistics sector

Kannout Eyad, Grodzki Michał, Grzegorowski Marek

Annals of Computer Science and Information Systems

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2022

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Vol. 30

403--412

EN

The industrial machine learning applications today involve developing and deploying MLOps pipelines to ensure the versatile quality of forecasting models over an extended period, simultaneously assuring the model's accuracy, stability, short training time, and resilience. In this study, we present the ML pipeline conforming to all the abovementioned aspects of models' quality formulated as a constrained multi-objective optimization problem. We also provide the reference implementation on state-of-the-art methods for data preprocessing, feature extraction, dimensionality reduction, feature and instance selection, model fitting, and ensemble blending. The experimental study on the real data set from the logistics industry confirmed the qualities of the proposed approach, as the successful participation in an international data competition did.

15

Multiobjective design of permanent magnet generator with dimensionality reduction in criteria space

Woźniak Piotr

Przegląd Elektrotechniczny

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2020

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R. 96, nr 7

86--91

EN

This paper addresses the problem of dimensionality reduction while preserving the characteristics of the Pareto set approximation in multiobjective optimization. The real-life engineering design problem for permanent magnet generator is considered. The Pareto front approximations with constraints, ranging from the five objectives to the set of two, are presented and compared.

PL

W artykule przedstawiono rozwiązanie problemu optymalizacji wielokryterialnej poprzez redukcję wymiarów w przestrzeni kryteriów. Rozważono właściwości zbioru Pareto w zadaniu projektowania generatora z magnesami trwałymi. Zaprezentowano i porównano aproksymacje frontu Pareto przy optymalizacji z ograniczeniami przy redukcji z pięciu dwóch kryteriów.

16

Novel approach for big data classification based on hybrid parallel dimensionality reduction using spark cluster

Ali Ahmed Hussein, Abdullah Mahmood Zaki

Computer Science

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2019

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Vol. 20 (4)

411--429

EN

The big data concept has elicited studies on how to accurately and efficiently extract valuable information from such huge dataset. The major problem during big data mining is data dimensionality due to a large number of dimensions in such datasets. This major consequence of high data dimensionality is that it affects the accuracy of machine learning (ML) classifiers; it also results in time wastage due to the presence of several redundant features in the dataset. This problem can be possibly solved using a fast feature reduction method. Hence, this study presents a fast HP-PL which is a new hybrid parallel feature reduction framework that utilizes spark to facilitate feature reduction on shared/distributed-memory clusters. The evaluation of the proposed HP-PL on KDD99 dataset showed the algorithm to be significantly faster than the conventional feature reduction techniques. The proposed technique required >1 minute to select 4 dataset features from over 79 features and 3,000,000 samples on a 3-node cluster (total of 21 cores). For the comparative algorithm, more than 2 hours was required to achieve the same feat. In the proposed system, Hadoop’s distributed file system (HDFS) was used to achieve distributed storage while Apache Spark was used as the computing engine. The model development was based on a parallel model with full consideration of the high performance and throughput of distributed computing. Conclusively, the proposed HP-PL method can achieve good accuracy with less memory and time compared to the conventional methods of feature reduction. This tool can be publicly accessed at https://github.com/ahmed/Fast-HP-PL.

17

Ensemble Methods for Improving Classification of Data Produced by Latent Dirichlet Allocation

Jankowski M.

Computer Science and Mathematical Modelling

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2018

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No. 8

17--28

EN

Topic models are very popular methods of text analysis. The most popular algorithm for topic modelling is LDA (Latent Dirichlet Allocation). Recently, many new methods were proposed, that enable the usage of this model in large scale processing. One of the problem is, that a data scientist has to choose the number of topics manually. This step, requires some previous analysis. A few methods were proposed to automatize this step, but none of them works very well if LDA is used as a preprocessing for further classification. In this paper, we propose an ensemble approach which allows us to use more than one model at prediction phase, at the same time, reducing the need of finding a single best number of topics. We have also analyzed a few methods of estimating topic number.

PL

Modelowanie tematyczne, jest popularną metodą analizy tekstów. Jednym z najbardziej popularnych algorytmów modelowania tematycznego jest LDA (Latent Dirichlet Allocation) [14]. W ostatnim czasie zostało zaproponowanych wiele nowych rozszerzeń tego modelu, które pozwalają na przetwarzanie dużych ilości danych. Jednym z problemów podczas użycia algorytmu LDA jest to, że liczba tematów musi zostać wybrana przed uruchomieniem algorytmu. Ten krok, wymaga wcześniejszej analizy i zaangażowania analityka danych. Powstało kilka metod, które pozwalają automatyzować ten krok, ale żadna z nich, nie działa dobrze, gdy LDA jest użyte do redukcji wymiarów przed klasyfikacją danych. W tej pracy, proponujemy podejście oparte o ensemble wielu modeli. Taki model, unika problemu wybrania jednego, najlepszego modelu LDA. Pokażemy, że takie podejście pozwala uzyskać niższy błąd klasyfikacji. Zaproponujemy również, dwie nowe metody wyboru liczby tematów, gdy chcemy użyć tylko pojedynczego modelu.

18

Intrinsic dimensionality detection criterion based on Locally Linear Embedding

Meng L., Breitkopf P.

Computer Science

|

2018

|

Vol. 19 (3)

345--356

EN

In this work, we revisit the Locally Linear Embedding (LLE) algorithm that is widely employed in dimensionality reduction. With a particular interest to the correspondences of the nearest neighbors in the original and embedded spaces, we observe that, when prescribing low-dimensional embedding spaces, LLE remains merely a weight-preserving rather than a neighborhood-preserving algorithm. Thus, we propose a \neighborhood-preserving ratio" criterion to estimate the minimal intrinsic dimensionality required for neighborhood preservation. We validate its efficiency on sets of synthetic data, including S-curve, Swiss roll, and a dataset of grayscale images.

19

Predicting pairwise relations with neural similarity encoders

Horn F., Müller K. R.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2018

|

Vol. 66, nr 6

821--830

EN

Matrix factorization is at the heart of many machine learning algorithms, for example, dimensionality reduction (e.g. kernel PCA) or recommender systems relying on collaborative filtering. Understanding a singular value decomposition (SVD) of a matrix as a neural network optimization problem enables us to decompose large matrices efficiently while dealing naturally with missing values in the given matrix. But most importantly, it allows us to learn the connection between data points’ feature vectors and the matrix containing information about their pairwise relations. In this paper we introduce a novel neural network architecture termed similarity encoder (SimEc), which is designed to simultaneously factorize a given target matrix while also learning the mapping to project the data points’ feature vectors into a similarity preserving embedding space. This makes it possible to, for example, easily compute out-of-sample solutions for new data points. Additionally, we demonstrate that SimEc can preserve non-metric similarities and even predict multiple pairwise relations between data points at once.

20

A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques

Nilashi M., Ibrahim O., Ahmadi H., Shahmoradi L., Farahmand M.

Biocybernetics and Biomedical Engineering

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2018

|

Vol. 38, no. 1

1--15

EN

Parkinson's Disease (PD) is a progressive degenerative disease of the nervous system that affects movement control. Unified Parkinson's Disease Rating Scale (UPDRS) is the baseline assessment for PD. UPDRS is the most widely used standardized scale to assess parkinsonism. Discovering the relationship between speech signal properties and UPDRS scores is an important task in PD diagnosis. Supervised machine learning techniques have been extensively used in predicting PD through a set of datasets. However, the most methods developed by supervised methods do not support the incremental updates of data. In addition, the standard supervised techniques cannot be used in an incremental situation for disease prediction and therefore they require to recompute all the training data to build the prediction models. In this paper, we take the advantages of an incremental machine learning technique, Incremental support vector machine, to develop a new method for UPDRS prediction. We use Incremental support vector machine to predict Total-UPDRS and Motor-UPDRS. We also use Non-linear iterative partial least squares for data dimensionality reduction and self-organizing map for clustering task. To evaluate the method, we conduct several experiments with a PD dataset and present the results in comparison with the methods developed in the previous research. The prediction accuracies of method measured by MAE for the Total-UPDRSand Motor-UPDRS were obtained respectively MAE = 0.4656 and MAE = 0.4967. The results of experimental analysis demonstrated that the proposed method is effective in predicting UPDRS. The method has potential to be implemented as an intelligent system for PD prediction in healthcare.