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The analysis of brand reputation and willingness to pay price premium with regression analysis and classification algorithms

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KYBERNETES 2025年第3期54卷 1532-1553页

作者： Ozhan, Seniz Ozhan, Erkan Habiboglu, Ozge Tekirdag Namik Kemal Univ Corlu Vocat Sch Tekirdag Turkiye Tekirdag Namik Kemal Univ Corlu Fac Engn Dept Comp Engn Tekirdag Turkiye Tekirdag Namik Kemal Univ Vocat Sch Social Sci Tekirdag Turkiye

Purpose - Brand reputation (BR) is one of the most important factors that affect the consumer-brand relationship and give businesses a competitive advantage. Businesses with a strong BR can increase their market shares and product market prices, in addition to gaining a competitive advantage. In order for businesses to have these advantages, they need to know and analyze their consumers. This study aimed to develop an alternative analysis method by using classification algorithms and regression analysis to measure and evaluate the effect of consumers' BR perceptions on their willingness to pay premium prices (WPP). Design/methodology/approach - The research data were collected from 483 participants by the online survey method due to the COVID-19 pandemic. The data were first analyzed with regression analysis, and the effect of BR on WPP was found to be significant. Then, using artificial intelligence (AI) methods that were not used in previous studies, consumers' perceptions of BR and WPP were clustered and classified. Findings - The results revealed the highest and lowest customer groups with BR and WPP and empirically demonstrated that highly accurate practical classification models can be applied to determine strategies in line with these findings. Originality/value - The model proposed in this study offers an integrated approach by using AI and regression analysis together and tries to fill the gap in the literature in this field. Therefore, the novelty of this study is to quantitatively reveal and evaluate the relationship between BR and WPP by using AI classification algorithms and regression analysis together.

关键词： Brand reputation (BR) Willingness to pay premium price (WPP) Artificial intelligence (AI) classification algorithms

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Distinguishing among standing postures with machine learning-based classification algorithms

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EXPERIMENTAL BRAIN RESEARCH 2025年第1期243卷 1-13页

作者： Rahimi, Negar Kamankesh, Alireza Amiridis, Ioannis G. Daneshgar, Sajjad Sahinis, Chrysostomos Hatzitaki, Vassilia Enoka, Roger M. Univ Colorado Boulder Dept Integrat Physiol Boulder CO 80309 USA Aristotle Univ Thessaloniki Dept Phys Educ & Sport Sci Serres Lab Neuromech Serres Greece Aristotle Univ Thessaloniki Dept Phys Educ & Sport Sci Lab Motor Behav & Adapted Phys Act Thessaloniki Greece

The purpose of our study was to evaluate the accuracy with which classification algorithms could distinguish among standing postures based on center-of-pressure (CoP) trajectories. We performed a secondary analysis of published data from three studies: Study A) assessment of balance control on firm or foam surfaces with eyes-open or closed, Study B) quantification of postural sway in forward-backward and side-to-side directions during four standing-balance tasks that differed in difficulty, and Study C) an evaluation of the impact of two modes of transcutaneous electrical nerve stimulation on balance control in older adults. Three classification algorithms (decision tree, random forest, and k-nearest neighbor) were used to classify standing postures based on the extracted features from CoP trajectories in both the time and time-frequency domains. Such classifications enable the identification of differences and similarities in control strategy. Our results, especially those involving time-frequency features, demonstrated that distinct CoP trajectories could be identified from the extracted features in all conditions and postures in each study. Although the overall classification accuracy was similar using time-frequency features (similar to 86%) for the three studies, there were substantial differences in accuracy across conditions and postures in Studies A and B but not in Study C. Nonetheless, the models were far superior to the published results with conventional metrics in distinguishing between the conditions and postures. Moreover, a Shapley Additive exPlanation analysis was able to identify the most important features that contributed to the classification performance of the models.

关键词： classification algorithms Standing posture Center of pressure Continuous wavelet transform Transcutaneous electrical nerve stimulation Shapley additive explanation

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Machine learning models and classification algorithms in the diagnosis of vestibular migraine: A systematic review and meta-analysis

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HEADACHE 2025年第4期65卷 695-708页

作者： Suarez-Barcena, Pablo D. Parra-Perez, Alberto M. Martin-Lagos, Juan Gallego-Martinez, Alvaro Lopez-Escamez, Jose A. Perez-Carpena, Patricia Hosp Univ San Cecilio Dept Otolaryngol Inst Invest Biosanit Ibs GRANADA Granada Spain Univ Granada Inst Invest Biosanitaria Otol & Neurotol Grp CTS495 Ibs GRANADA Granada Spain CIBERER Ctr Invest Biomed Red Enfermedades Raras Sensorineural Pathol Programme Madrid Spain Univ Granada Dept Surg Div Otolaryngol Inst Invest Biosanitariaibs GRANADA Granada Spain Univ Seville Univ Pablo Olavide CS USE UPO Ctr Andaluz Biol Mol & Med Regenerat CABIMER Consejo Super Invest Cient Av Americo Vespucio Seville Spain Univ Sydney Kolling Inst Fac Med & Hlth Sch Med SciMenieres Dis Neurosci Res Program Sydney NSW Australia

ObjectivesTo perform a systematic review and meta-analysis to evaluate the effectiveness of machine learning (ML) algorithms in the diagnosis of vestibular *** to the absence of defined biomarkers for diagnosing vestibular migraine (VM), it is valuable to determine which clinical, physical, and exploratory information is most crucial to diagnosing this disease. The use of artificial intelligence tools could streamline this *** systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and searched for records from PubMed, Scopus, and Web of Science. Observational (case-control and cohort) studies were included to assess the ability of artificial intelligence (AI) to distinguish VM from other vestibular disorders. Risk of bias and applicability concerns were assessed using the Quality Assessment of Diagnostic Accuracy Studies-AI *** total of 14 articles were included in the systematic review, and 10 were eligible for meta-analysis. The main inputs included for the ML algorithms were anamnesis (medical history), physical examination, results from audiological and vestibular tests, and imaging. The global sensitivity was 0.85 (95% confidence interval [CI] 0.73-0.92, I2 = 96%), while the global specificity was 0.89 (95% CI 0.84-0.93, I2 = 95%). The pooled diagnostic odds ratio was 48.15 (95% CI 17.64-131.43, I2 = 97%). Using the bivariate model, the area under the curve and for the summary receiver operating characteristic curve, using the 10 available studies, was 0.94 (95% CI 0.86-0.96).ConclusionMachine learning algorithms could be used as effective tools for the diagnosis process in VM. The use of models trained with three to four inputs yield the highest accuracy, compared to other strategies. However, the design and validation of these studies could be improved to ensure the reproducibility and generalizability of results.

关键词： classification algorithms differential diagnosis machine learning vestibular migraine

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Combination ATR-FTIR with Multiple classification algorithms for Authentication of the Four Medicinal Plants from Curcuma L. in Rhizomes and Tuberous Roots

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SENSORS 2025年第1期25卷 50-50页

作者： Wen, Qiuyi Wei, Wenlong Li, Yun Chen, Dan Zhang, Jianqing Li, Zhenwei Guo, De-an Guangdong Pharmaceut Univ Sch Pharm Guangzhou 510006 Peoples R China Chinese Acad Sci Zhongshan Inst Drug Discovery Shanghai Inst Mat Med Zhongshan 528400 Peoples R China Nanjing Univ Chinese Med Sch Chinese Mat Med Nanjing 210023 Peoples R China

Curcumae Longae Rhizoma (CLRh), Curcumae Radix (CRa), and Curcumae Rhizoma (CRh), derived from the different medicinal parts of the Curcuma species, are blood-activating analgesics commonly used for promoting blood circulation and relieving pain. Due to their certain similarities in chemical composition and pharmacological effects, these three herbs exhibit a high risk associated with mixing and indiscriminate use. The diverse methods used for distinguishing the medicinal origins are complex, time-consuming, and limited to intraspecific differentiation, which are not suitable for rapid and systematic identification. We developed a rapid analysis method for identification of affinis and different medicinal materials using attenuated total reflection-Fourier-transform infrared spectroscopy (ATR-FTIR) combined with machine learning algorithms. The original spectroscopic data were pretreated using derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and smoothing (S) methods. Among them, 1D + MSC + 13S emerged as the best pretreatment method. Then, t-distributed stochastic neighbor embedding (t-SNE) was applied to visualize the results, and seven kinds of classification models were constructed. The results showed that support vector machine (SVM) modeling was superior to other models and the accuracy of validation and prediction was preferable, with a modeling time of 127.76 s. The established method could be employed to rapidly and effectively distinguish the different origins and parts of Curcuma species and thus provides a technique for rapid quality evaluation of affinis species.

关键词： Curcumae Radix classification algorithms ATR-FTIR species identification

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Analyzing classification algorithms for Network Intrusion Detection

Analyzing Classification Algorithms for Network Intrusion De...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Barkha Nandwana Himanshu Pandey Divya Chauhan Saloni Kumari Sharda University Greater Noida Uttar Pradesh India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Anomaly detection systems can identify unknown attacks, butless precise and frequently raise false alarms. This research looks at machine learning methods to develop intrusion detection systems that may be applied to current computer networks. First, a three-step optimization method is shown to improve the quality of the detection: 1) using improved data to rebalance the dataset, 2) training various models and 3) combining the output of the top models. The models used in this approach are trained on known attacks, and hence anomaly detection is not possible. We examined the sensitivity, accuracy, false positive rate, ROC curve, and other general classification algorithms of several binary classifiers, including Naive Bayes, Linear SVM, Random Forest, and XGBoost, to address current problems. Our findings suggested that some improvements could be made to current models. In order to defend against future attacks,we plan to utilize the Artificial Neural Network (ANN) approach, a deep learning technique, for detecting attacks and retaining them in long-term memory.

关键词： Support vector machines Training Technological innovation Computational modeling Network intrusion detection Data models classification algorithms Bayes methods Random forests Anomaly detection

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Research on Image Data classification algorithms Based on Deep Learning

Research on Image Data Classification Algorithms Based on De...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Fengyu Weng Liqiang Liu Haonan Guo Chengxin Wang Engineering University of PAP Xi'an Shaanxi China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This paper presents an image data classification algorithm based on deep learning. By integrating a Convolutional Block Attention Module (CBAM) into the ResNet-50 backbone, we form an enhanced attention residual module and validate it on the CUB-200-2011 dataset. Transfer learning is employed using ImageNet pre-trained weights, with images uniformly resized and randomly cropped to ensure consistent input. Training utilizes a stochastic gradient descent (SGD) optimizer, combined with a cosine annealing strategy for dynamic learning rate adjustment, and incorporates data augmentation techniques such as random flipping and color jitter to improve model generalization. The findings from the experiments show that the model markedly improves the effectiveness of classification. It accurately locates key areas in complex backgrounds and effectively captures local detailed features, thereby improving the learning of important features and reducing background noise interference. Ablation studies show that introducing the attention mechanism significantly boosts model accuracy, while combining channel grouping attention further enhances semantic feature learning. These findings highlight the proposed method's superiority in finegrained image classification tasks.

关键词： Deep learning Training Accuracy Attention mechanisms Transfer learning Stochastic processes Data models classification algorithms Convolutional neural networks Image classification

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Hyper-parameters tuning and dimension reduction effects on heart disease prediction using classification algorithms

Progress in Engineering Science

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Progress in Engineering Science 2025年第1期2卷

作者： Divandari, Mohammad Ghabi, Delaram Ebrahimi, Mojtaba Khorsandi Department of Electrical Engineering Aliabad Katoul Branch Islamic Azad University Aliabad Katoul Iran Energy Research Center Aliabad Katoul Branch Islamic Azad University Aliabad Katoul Iran

Heart complication is one of the most dangerous diseases that take many victims all around the world. This paper presented heart disease prediction based on machine learning algorithms. In this method, hyper-parameters tuning and dimension reduction effects on heart disease prediction has been presented. In addition, six classifiers: K-nearest neighbors (KNN), support vector classifier (SVC), Gaussian Naïve Bayes (Gaussian NB), logistic regression (LR), decision tree (DT), random forest (RF) and 3 types of dimensional reduction models based on principal component analysis (PCA), T-distributed stochastic neighbor embedding (TSNE), linear discriminant analysis (LDA) and grid search cross validation (GridSearchCV) as an automated hyper-parameter tuning algorithm are employed to help in prediction of cardiovascular diseases (SVCs). In Jupyter Notebook, 1025 patients using 14 attributes are utilized that 30 % of them are the test data. The results of six classifiers are evaluated based on evaluation metrics containing precision, accuracy, recall, F1-score, confusion matrix and receiver operating characteristic (ROC) curve. The results of proposed method demonstrate high performance with 99.8 % accuracy for DT and RF and 99.02 % accuracy for KNN. © 2024 Elsevier B.V.

关键词： classification algorithms dimension reduction heart disease hyper-parameter tuning prediction

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classification algorithms IN PATTERN RECOGNITION

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IEEE TRANSACTIONS ON AUDIO AND ELECTROACOUSTICS 1968年第2期AU16卷 203-+页

作者： NAGY, G IBM Thomas J. Watson Research Center Yorktown Heights NY USA

Linear and nonlinear methods of pattern classification which have been found useful in laboratory investigations of various recognition tasks are reviewed. The discussion includes correlation methods, maximum likelihood formulations with independence or normality assumptions, the minimax Anderson-Bahadur formula, trainable systems, discriminant analysis, optimal quadratic boundaries, tree and chain expansions of binary probability density functions, and sequential decision schemes. The area of applicability, basic assumptions, manner of derivation, and relative computational complexity of each algorithm are described. Each method is illustrated by means of the same two-class two-dimensional numerical example. The "training set" in this example comprises four samples from either class; the "test set" is the set of all points in the normal distributions characterized by the sample means and sample covariance matrices of the training set. Procedural difficulties stemming from an insufficient number of samples, various violations of the underlying statistical models, linear nonseparability, noninvertible covariance matrices, multimodal distributions, and other experimental facts of life are touched on.

关键词： classification algorithms Pattern recognition Covariance matrix Pattern classification Laboratories Correlation Minimax techniques Probability density function Computational complexity Testing

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classification algorithms Framework (CAF) to Enable Intelligent Systems Using JetBrains MPS Domain-Specific Languages Environment

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IEEE ACCESS 2020年 8卷 14832-14840页

作者： Meacham, Sofia Pech, Vaclav Nauck, Detlef Bournemouth Univ Dept Comp & Informat Poole BH12 5BB Dorset England JetBrains MPS Prague 14000 Czech Republic British Telecommun PLC Adastral Pk England

This paper describes the design and development of a classification algorithms Framework (CAF) using the JetBrains MPS domain-specific languages (DSLs) development environment. It is increasingly recognized that the systems of the future will contain some form of adaptivity therefore making them intelligent systems as opposed to the static systems of the past. These intelligent systems can be extremely complex and difficult to maintain. Descriptions at higher-level of abstraction (system-level) have long been identified by industry and academia to reduce complexity. This research presents a Framework of classification algorithms at system-level that enables quick experimentation with several different algorithms from Naive Bayes to Logistic Regression. It has been developed as a tool to address the requirements of British Telecom's (BT's) data-science team. The tool has been presented at BT and JetBrains MPS and feedback has been collected and evaluated. Beyond the reduction in complexity through the system-level description, the most prominent advantage of this research is its potential applicability to many application contexts. It has been designed to be applicable for intelligent applications in several domains from business analytics, eLearning to eHealth, etc. Its wide applicability will contribute to enabling the larger vision of Artificial Intelligence (AI) adoption in context.

关键词： classification algorithms domain-specific languages framework intelligent systems

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classification algorithms for Damage Identification in Framed Structures by Means of Natural Frequencies 10th

Classification Algorithms for Damage Identification in Frame...

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10th International Operational Modal Analysis Conference (IOMAC)

作者： Sepe, Vincenzo Buccella, Armando Potenza, Francesco Diaferio, Mariella Univ G dAnnunzio Dept Engn & Geol I-65127 Pescara Italy Univ G dAnnunzio Res Ctr UdA TechLab Pescara Italy Polytech Univ Bari Dept Civil Environm Land Bldg Engn & Chem I-70125 Bari Italy

ISBN: (纸本)9783031614279;9783031614255;9783031614248

The identification and localization of structural damage plays an important role in the health monitoring of civil structures. However, when the number of potentially damaged structures is high it can be difficult to distinguish which ones have actually suffered damage and where further investigations need to be directed. It can be therefore useful to have procedures and algorithms that allow indicating automatically, as a preliminary step of deeper investigations, the possible occurrence of damage and its approximate position. In this context, the paper explores the variations of the natural frequencies for plane and 3D framed structures due to localized damage within macro-regions into which the structure can be divided. Based on numerically generated data, it is shown how the position of the damage within different macro-regions produces different and recognizable scenarios with respect to the variations of the natural frequencies. This allows to make use of classic data classification procedures, through which it is possible to automatically obtain indications on the macro-region of the structure subject to damage, thus limiting the extent of the investigations to be carried out on site.

关键词： Health monitoring of framed structures Damage identification classification algorithms Machine learning Vibration-based procedures

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