Wyniki wyszukiwania - BazTech

1

A hybrid fuzzy approach for selecting digital 3pl service providers

Şahin Aybike E., Mercangöz Burcu Adigüzel, Köseoğlu Sinem Derindere

LogForum

|

2025

|

Vol. 21, no 2

183--196

EN

Background: The study focuses on identifying the technologies, capabilities, and characteristics that logistics firms must possess during the digital transformation of the supply chain. It addresses the challenge of determining the necessary criteria for selecting a technological or digital third-party logistics service provider (3PL). It aims to develop a structured and hybrid decision-making framework for selecting digital 3PL service providers by integrating subjective and objective weighting methods. The scientific purpose is to bridge the gap in existing research by incorporating Multi-Criteria Decision-Making (MCDM) methods and providing a more comprehensive, empirically validated, and practically applicable model that enhances digital supply chain efficiency. Methods: The study began with a literature review to identify relevant technological indicators. These indicators were then presented to a group of experts in academia, resulting in the identification of six main criteria and 21 sub-criteria. To determine the weights of these criteria and sub-criteria, MCDM methods were used. Experts with experience in Turkey were selected for the analyses. The study first applied SWARA and Fuzzy Entropy methods, followed by a Bayesian approach. Results: The findings reveal that the most significant factors influencing the selection of a technological 3PL provider include: Automatic Material Placement and Tracking Technology, Lack of Digital Collaboration, Technology Compatibility, Supplier Compatibility and Use of Augmented and Virtual Reality Technologies. These factors play a key role in ensuring effective digital integration within the supply chain. Conclusion: This study integrates SWARA, Fuzzy Entropy, and a Bayesian approach to propose a novel decision-making model for selecting digital 3PL service providers. It highlights key criteria such as technology compatibility and augmented reality technologies, emphasizing their importance for seamless digital supply chain integration. SWARA offers a subjective weighting mechanism, Fuzzy Entropy ensures objectivity, and the Bayesian approach balances these methods. The study provides valuable insights into the factors that influence digital transformation in 3PL services and offers actionable policy recommendations to accelerate this transformation in supply chain management.

2

Moving object detection: a new method combining background subtraction, fuzzy entropy thresholding and differential evolution optimization

Boufares Oussama, Boussif Mohamed, Saadaoui Wajdi, Miraoui Imed

Acta Mechanica et Automatica

|

2025

|

Vol. 19, no. 1

106--116

EN

Detecting moving objects in videos is an evolving area of research, with important implications in many computer vision applications. In this paper, we propose a new detection approach by combining background subtraction and multi-level image thresholding based on fuzzy entropy, powered by the differential evolution (DE) algorithm. The first step of our method is background subtraction, aiming to isolate moving objects by eliminating the static background. However, this approach can be sensitive to lighting variations and background changes, thus limiting its accuracy. To overcome these limitations, we introduce multi-level image thresholding based on fuzzy entropy. This method exploits the intrinsic variability of moving objects rather than simply differentiating against the background. By adjusting thresholds locally, our approach better adapts to changing environmental conditions. The key element of our proposal lies in the optimization of the fuzzy entropy threshold parameters using the differential evolution algorithm. We chose DE for its robustness and efficiency in handling continuous optimization problems, which makes it well-suited for complex tasks like multi-level image threshold-ing. By iteratively adjusting the thresholds, we maximize the detection of moving objects while minimizing false positives, thereby improving the robustness and accuracy of the method. Our experiments on test video sequences demonstrate the effectiveness of our approach, highlighting a significant improvement in moving object detection compared to traditional methods. This promising methodology paves the way for future advances in moving object detection, with potential applications in surveillance, robotics, and computer vision in general.

3

Scattering transform-based features for the automatic seizure detection

Jiang Yun, Chen Wanzhong, You Yang

Biocybernetics and Biomedical Engineering

|

2020

|

Vol. 40, no. 1

77--89

EN

Developing the automatic detection system is of great clinical significance for assisting neurologists to detect epilepsy using electroencephalogram (EEG) signals. In this research, we explore the ability of a newly-developed algorithm named scattering transform in seizure detection. The preprocessed signal is initially decomposed into scattering coefficients with various orders and scales employing scattering transform. Fuzzy entropy (FuzzyEn) and Log energy entropy (LogEn) of the sub-band coefficients are obtained to characterize the epileptic seizure signals. Then the joint features are fed into five classifiers including support vector machine (SVM), least squares-support vector machine (LS-SVM), genetic algorithm-support vector machine (GA-SVM), extreme learning machine (ELM) and probabilistic neural network (PNN) for the verification of the effectiveness of the proposed scheme. Finally, we not only compare the classification results and the time efficiency derived from different classifiers, but also explore the discrimination performance of the proposed methodology based on ten different classification tasks with great clinical significance. The prominent classification accuracy (ACC) of 99.87 %, 99.59 %, 99.58 %, 99.56 % and 99.80 % are achieved using the above five classifiers respectively. The average ACC and Matthews correlation coefficient (MCC) of 99.75 % and 0.99 are also yielded based on all tasks. Furthermore, the result of Kruskal-Wallis Test for the verification of statistical significance confirms the reliability of the proposal. The comparison with the latest state-of-the-art techniques indicates the superior performance of the proposal. A tradeoff between classification accuracy and time complexity of the proposed approach is accomplished in our work and the possibility for clinical application is also demonstrated.

4

Application of intrinsic band function technique for automated detection of sleep apnea using HRV and EDR signals

Tripathy R. K.

Biocybernetics and Biomedical Engineering

|

2018

|

Vol. 38, no. 1

136--144

EN

Sleep apnea is the most common sleep disorder that causes respiratory, cardiac and brain diseases. The heart rate variability (HRV) and the electrocardiogram-derived respiration (EDR) signals to capture the cardio-respiratory information and the features extracted from these two signals have been used for the detection of sleep apnea. Detection of sleep apnea using the combination of HRV and EDR signals may provide more information. This paper proposes a novel method for the automated detection of sleep apnea based on the features extracted from HRV and EDR signals. The method involves the extraction of features from the intrinsic band functions (IBFs) of both EDR and HRV signals, and the classification using kernel extreme learning machine (KELM). The IBFs of HRV and EDR signals are evaluated using the Fourier decomposition method (FDM). The energy and the fuzzy entropy (FE) features are extracted from these IBFs. The kernel extreme learning machine (KELM) classifier with four kernel functions such as 'linear', 'polynomial', 'radial basis function (RBF)' and 'cosine wavelet kernel' is used for the automated detection of sleep apnea. The proposed technique yielded a sensitivity and a specificity of 78.02% and 74.64%, respectively using the public database. The method outperformed some of the reported works using HRV and EDR signals.

5

Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM

Li M., Chen W., Zhang T.

Biocybernetics and Biomedical Engineering

|

2016

|

Vol. 36, no. 4

708--718

EN

Aiming at the problems of low accuracy, poor universality and functional singleness for seizure detection, an effective approach using wavelet-based non-linear analysis and genetic algorithm optimized support vector machine (GA-SVM) is proposed to deal with five challenging classification problems in this study. Instead of the traditional discrete wavelet transform (DWT), we attempt to explore the ability of double-density discrete wavelet transform (DD-DWT) to decompose the original EEG into specific sub-bands. The Hurst exponent (HE) and fuzzy entropy (FuzzyEn) are extracted as input features and then fed into two classifiers. On using these ranking non-linear features, the GA-SVM configured with fewer features is found to achieve the prominent classification performance for various combinations such as AB-CD-E, A-D-E, ABCD-E, C-E and D-E, achieving accuracies of 99.36%, 99.60%, 99.40%, 100% and 100%, respectively. The results have indicated that our scheme is not only appropriate in solving problems with multiple classes but also of lower complexity and better expansibility. These characteristics would make this method become an attractive alternative for actual clinical diagnosis.

6

Badanie klasyfikatora rozmytego z wykorzystaniem entropii rozmytej

Sosnowski Z. A.

Symulacja w Badaniach i Rozwoju

|

2012

|

Vol. 3, nr 2

99--106

PL

W pracy przedstawiono architekturę klasyfikatora rozmytego opartego na entropii rozmytej oraz zbadano jego wydajność na standardowych zestawach danych: Iris i Wisconsin breast cancer. Wyniki symulacji pokazują, że przedstawiony klasyfikator daje zadawalające wskaźniki klasyfikacji.

EN

In this paper, we present the architecture of fuzzy classifier based on fuzzy entropy and examine its performance on Iris and Wisconsin breast cancer data sets. Simulation results show that the presented classifier has a satisfactory classification rate.