Decision Analytics Journal最新文献

{"title":"A novel fusion Support Vector Machine integrating weak and sphere models for classification challenges with massive data","authors":"Jonatha Sousa Pimentel ,&nbsp;Raydonal Ospina ,&nbsp;Anderson Ara","doi":"10.1016/j.dajour.2024.100457","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100457","url":null,"abstract":"<div><p>The unprecedented growth in data generation has necessitated the adoption of advanced analytical techniques. Support Vector Machine (SVM) is a powerful machine learning tool that has proven invaluable in classifying observations through optimal hyperplane in higher dimensions. Despite their widespread use, SVM models encounter substantial challenges during the learning phase with massive datasets, necessitating strategic modifications. This paper introduces a novel fusion methodology incorporating weak and sphere support vector machine to address classification challenges with massive datasets. Comparative analyses across diverse simulated and benchmark real datasets underscore the efficacy of the proposed methodologies, exhibiting sustained predictive performance. The remarkable efficiency gain is noteworthy, as the learning phase requires only 10% of the computation time compared to conventional SVM approaches.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100457"},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000614/pdfft?md5=cf8e027f895d692040e17e723777b8a5&pid=1-s2.0-S2772662224000614-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140639039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic hesitant fuzzy multiple criteria decision-making with triangular norm based similarity and entropy measures","authors":"B. Farhadinia ,&nbsp;M. Abdollahian ,&nbsp;U. Aickelin","doi":"10.1016/j.dajour.2024.100465","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100465","url":null,"abstract":"<div><p>Existing probabilistic hesitant fuzzy set (PHFS) measures are constructed using two information measures: hesitancy and unwrapped probabilities. We argue that unifying these semantic terms in PHFS information theory is not logical. We introduce a new class of information measures for PHFSs, which address the logical wrapping of hesitant fuzzy sets (HFS) and probability. We propose several similarity measures for these sets that use the Triangular norm operator. We consider the relationship between measures of entropy and similarity and represent the axiomatic definition of PHFS entropy measures. Finally, we use case studies to demonstrate applications of these information measures. We describe two multiple-criteria decision-making algorithms. The last step is devoted to PHFS ranking procedures: one based on the score function of alternatives and the other based on the relative closeness of alternatives. This contribution describes new information measures and uses case studies to illustrate how they can be applied to decision-making processes.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100465"},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000699/pdfft?md5=d052cd2d10e3112fe849bb77390b6925&pid=1-s2.0-S2772662224000699-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140604564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A workload prediction model for reducing service level agreement violations in cloud data centers","authors":"P. Nehra,&nbsp;Nishtha Kesswani","doi":"10.1016/j.dajour.2024.100463","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100463","url":null,"abstract":"<div><p>Cloud computing has become an emerging technology that offers services based on the pay-as-usage model. The cloud provides several advantages, but these advantages come with challenges, such as reducing Service Level Agreement (SLA) violations, efficient resource utilization, reducing energy consumption, etc., needing attention to leverage customer satisfaction and benefit cloud service providers. Workload prediction is a strategy that provides many benefits: reduced SLA violation, resource scaling, and resource optimization by predicting future workload. However, due to the varying workload of cloud applications, it is difficult to predict the workload accurately, and it fails for long-term dependencies. We propose a methodology based on Multiplicative Long Short Term Memory (mLSTM) that allows input-dependent transitions and considers long-term dependencies to predict the workload to address this issue. The proposed method is implemented and compared with other variants of LSTM used in literature for workload prediction purposes. The proposed work outperforms existing variants of LSTM in terms of prediction accuracy.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100463"},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000675/pdfft?md5=aea414c2a3ca9ece24ada4b1856e462a&pid=1-s2.0-S2772662224000675-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Natural Language Processing-Based Classification and Mode-Based Ranking of Musculoskeletal Disorder Risk Factors","authors":"Md Abrar Jahin,&nbsp;Subrata Talapatra","doi":"10.1016/j.dajour.2024.100464","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100464","url":null,"abstract":"<div><p>This research explores the intricate landscape of Musculoskeletal Disorder (MSD) risk factors, employing a novel fusion of Natural Language Processing (NLP) techniques and mode-based ranking methodologies. Enhancing knowledge of MSD risk factors, their classification, and their relative severity is the main goal of enabling more focused preventative and treatment efforts. The study benchmarks eight NLP models, integrating pre-trained transformers, cosine similarity, and various distance metrics to categorize risk factors into personal, biomechanical, workplace, psychological, and organizational classes. Key findings reveal that the Bidirectional Encoder Representations from Transformers (BERT) model with cosine similarity attains an overall accuracy of 28%, while the sentence transformer, coupled with Euclidean, Bray–Curtis, and Minkowski distances, achieves a flawless accuracy score of 100%. Using a 10-fold cross-validation strategy and performing rigorous statistical paired t-tests and Cohen’s d tests (with a 5% significance level assumed), the study provides the results with greater validity. To determine the severity hierarchy of MSD risk variables, the research uses survey data and a mode-based ranking technique parallel to the classification efforts. Intriguingly, the rankings align precisely with the previous literature, reaffirming the consistency and reliability of the approach. “Working posture” emerges as the most severe risk factor, emphasizing the critical role of proper posture in preventing MSD. The collective perceptions of survey participants underscore the significance of factors like “Job insecurity”, “Effort reward imbalance”, and “Poor employee facility” in contributing to MSD risks. The convergence of rankings provides actionable insights for organizations aiming to reduce the prevalence of MSD. The study concludes with implications for targeted interventions, recommendations for improving workplace conditions, and avenues for future research. This holistic approach, integrating NLP and mode-based ranking, contributes to a more sophisticated comprehension of MSD risk factors and opens the door for more effective strategies in occupational health.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100464"},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000687/pdfft?md5=403514e038f7265fdcbe649e4a73e70c&pid=1-s2.0-S2772662224000687-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140558153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A structured approach for enhancing clinical risk monitoring and workflow digitalization in healthcare","authors":"Leonardo Longo,&nbsp;Orazio Tomarchio,&nbsp;Natalia Trapani","doi":"10.1016/j.dajour.2024.100462","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100462","url":null,"abstract":"<div><p>A multitude of studies have highlighted alarming data on healthcare adverse events and medical errors in many countries, including Italy. Many of these errors appear to be the result of non-compliance with good practices and organizational operating procedures, specified in ministerial directives and international standards. This paper describes the efforts of an Italian research project to propose a structured approach to healthcare process management, emphasizing clinical risk management. Using the Business Process Model and Standard Notation (BPMN), the project models clinical processes and integrates them into the clinical information system to identify process deficiencies, highlight compliance vulnerabilities, monitoring and managing clinical risks. The workflows’ digitalization, enabled the definition and calculation of several Key Performance Indicators (KPIs) for a long-term evaluation of the success of the modeled processes. In the experimental phase, the project allowed to identify areas significantly affected by operational deviations, enabling targeted actions to safeguard patient health and generate economic savings due to the reduction of legal actions against medical workers’ errors. The effects of an overall improvement on quality of care can be appreciated not only by patients but also by medical and administrative staff of the clinics.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100462"},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000663/pdfft?md5=71aedb301a0c3a1eab4c21c92a1113d3&pid=1-s2.0-S2772662224000663-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140604563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative analysis of machine learning algorithms for detecting COVID-19 using lung X-ray images","authors":"Susmita Hamal ,&nbsp;Bhupesh Kumar Mishra ,&nbsp;Robert Baldock ,&nbsp;William Sayers ,&nbsp;Tek Narayan Adhikari ,&nbsp;Ryan M. Gibson","doi":"10.1016/j.dajour.2024.100460","DOIUrl":"10.1016/j.dajour.2024.100460","url":null,"abstract":"<div><p>Machine intelligence has the potential to play a significant role in diagnosing, managing, and guiding the treatment of disease, which supports the rising demands on healthcare to provide rapid and accurate interpretation of clinical data. The global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus (SARSCoV-2) exposed a need for rapid clinical data interpretation in response to an unprecedented burden on the healthcare system. A new healthcare challenge has arisen – post-COVID syndrome or ‘long COVID’. Symptoms of the post-COVID syndrome can persist for months following infection with SARS-CoV-2, often characterised by fatigue, breathlessness, dizziness, and pain. Despite this additional healthcare burden, no tests can diagnose, monitor, or determine the efficacy of treatments/interventions to support recovery. In this paper, an array of machine-learning algorithms is trained to evaluate and detect COVID-19-associated changes to lung tissue from X-ray images. X-ray images are classified from open sources into three categories: COVID-19 patients, patients with pneumonia, and unaffected otherwise healthy individuals using existing Machine Learning (ML) and pre-trained deep learning models. Prioritising models with the fewest false positives and false negatives assessed the performance of different models in detecting COVID-19-associated lung tissue. In addition, image pre-processing, data augmentation, and hyperparameter tuning are used to achieve the best accuracy in the models. Different ML models, including K Nearest Neighbour (KNN), and decision trees (DT), as well as transfer learning models such as Convolutional Neural Network (CNN), Visual Geometry Group (VGG-16, VGG-19), ResNet50, DenseNet201, Xception, and InceptionV3, were tested to evaluate the performance of these models for X-ray images classification. The comparative analysis indicates that VGG-19 with augmentation performed best among the ten algorithms with a training accuracy of 99%, testing accuracy of 98%, and precision of 90% for COVID-19, 90% for normal, and 100% for pneumonia. This higher accuracy for detecting COVID-19-associated lung changes on X-ray may be further developed to stratify patients suffering from post-COVID syndrome. This may enable future intervention studies to determine the efficacy of treatments or better track patients’ prognoses to be optimised.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100460"},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277266222400064X/pdfft?md5=3d2f660c704a3ba675dcd7f70983eb3a&pid=1-s2.0-S277266222400064X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140766321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient multi-stage ensemble deep learning framework for diagnosing infectious diseases","authors":"Rohit Kumar Bondugula,&nbsp;Nitin Sai Bommi,&nbsp;Siba K. Udgata","doi":"10.1016/j.dajour.2024.100458","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100458","url":null,"abstract":"<div><p>This study presents an efficient four-stage ensemble deep learning framework for diagnosing infectious diseases. The model is evaluated on three standard datasets. In our proposed four-stage transfer learning-based deep neural architecture (4s-min-FN), the images pass through four stages, each attempting to classify images as positive. A negative class is confirmed if every stage classifies the image as negative. This model (4S-min-FN) ensures the minimization of false negatives. When the new cases go through a changing scenario, the same model is modified (4S-min-FP) to minimize false positives following the same architecture but with a different transition rule. We use an adaptive threshold setting in the proposed architecture to find a proper trade-off between sensitivity, specificity, and good accuracy. We use well-known pre-trained deep neural architectures like Inception, ResNet-50, DenseNet-121, and MobileNet for the four-stage experimental evaluation and predicted the class, which provided better insights about the condition. The proposed model can perform at par with the existing techniques in terms of accuracy while reducing false positives and negatives depending on the requirement.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100458"},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000626/pdfft?md5=22ca4cc78efbb22a2e6adc1424a55d51&pid=1-s2.0-S2772662224000626-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140543567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning approach for unraveling the influence of air quality awareness on travel behavior","authors":"Kapil Kumar Meena ,&nbsp;Deepak Bairwa ,&nbsp;Amit Agarwal","doi":"10.1016/j.dajour.2024.100459","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100459","url":null,"abstract":"<div><p>Urbanization has escalated air pollution levels with subsequent health implications. This study explores the potential of awareness about air quality levels on travelers’ choices and proposes machine learning models to predict travel mode under exposure to different air quality levels. These models are Random Forest, XGBoost, Naive Bayes (NB), K-Nearest Neighbor, Support Vector Machine (SVM), and Multinomial Logistic Regression (MLR). The models are trained using data from individuals who have an understanding of air quality levels. The trained model is further used to predict travel mode choices when the knowledge of air quality reaches all travelers. Travel modes are aggregated into open/closed modes, private/public modes, and motorized/non-motorized/metro modes to assess the impact of air quality awareness and modal shift. The model evaluation shows that the Random forest (RF) exhibits the highest accuracy and F1 score. The model demonstrates that as air quality worsens, commuters shift their preferences from open modes of transport to closed modes. Similarly, during periods of deteriorating air quality, commuters exhibit a preference for public transportation over private modes. This study emphasizes the crucial role of disseminating air quality information, empowering individuals to make informed travel decisions, and mitigating health risks.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100459"},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000638/pdfft?md5=85e016d8cf495261e4d9a8bc14948218&pid=1-s2.0-S2772662224000638-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140540808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel multi-criteria approach for evaluating social discrimination in OECD countries","authors":"Osman Pala","doi":"10.1016/j.dajour.2024.100456","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100456","url":null,"abstract":"<div><p>The Organisation for Economic Co-operation and Development (OECD) was established to unite countries from different geographical regions to achieve prosperity and economic progress. Different geographical regions have different cultures. The sociological perspective on gender varies in these cultures. As a result, gaps occur in terms of gender discrimination and its dimensions between countries. While social progress is a prerequisite for economic progress, society must be in harmony for social progress. The Social Institutions and Gender Index (SIGI) was developed to measure progress in different countries. This study proposes a Multi-Criteria Decision-Making (MCDM) model over the SIGI index to measure gender discrimination levels in OECD countries. However, since the differences in gender discrimination resulting from the multicultural structure of OECD lead to significant variability in terms of countries, countries are first clustered. Then, SIGI criteria significance levels are evaluated with a novel Importance of Criteria’s Indifference to Normalization (ICIN) method based on the differences between the two normalization approaches. Subsequently, a novel Modified Extended Alternative Ranking Order Method Accounting for Two-Step Normalization (ME-AROMAN) approach is employed to rank the member countries. While discrimination in the family is the most prominent factor for countries better at social equality and access to productive and financial assets is the major factor that separates the countries worse at social equality, the sensitivity and comparative analysis demonstrate the applicability and validity of ICIN and ME-AROMAN in MCDM literature.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100456"},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000602/pdfft?md5=7eaccf14a289eb512aa98e41ea9f8a39&pid=1-s2.0-S2772662224000602-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140547401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A unified theory of acceptance and use of technology and fuzzy artificial intelligence model for electric vehicle demand analysis","authors":"Ahmet Faruk Aysan ,&nbsp;Serhat Yüksel ,&nbsp;Serkan Eti ,&nbsp;Hasan Dinçer ,&nbsp;Mahmut Selami Akin ,&nbsp;Hakan Kalkavan ,&nbsp;Alexey Mikhaylov","doi":"10.1016/j.dajour.2024.100455","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100455","url":null,"abstract":"<div><p>This study aims to reveal consumers’ intention to purchase Electric Vehicles (EVs) based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A hybrid fuzzy decision-making model with three stages is proposed. First, the experts’ weights are computed using an artificial intelligence methodology. Second, eight UTAUT-based indicators are examined using a T-Spherical TOPSIS-based DEMATEL (TOP-DEMATEL) methodology. The criteria are weighted by using multi-SWARA (M-SWARA) methodology. Third, an evaluation is conducted for the seven emerging countries by considering a Spherical Fuzzy (SF) Additive Ratio Assessment (ARAS) technique. The main contribution of this study is that a new decision-making methodology can identify more significant determinants of intention to use EVs. The methodological contribution of this study is integrating artificial intelligence methodology with fuzzy decision-making theory. The findings demonstrate that environmental factors play the most significant role in the intention to use EVs. Additionally, performance expectancy is also another critical determinant. We also find environmental issues should also be given importance in the production process of EVs. Using fossil fuels while producing these vehicles will significantly reduce users’ confidence. This phenomenon will cause consumers with environmental awareness not to purchase these vehicles.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100455"},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000596/pdfft?md5=3e798a210b78955f5feb468d42954c8a&pid=1-s2.0-S2772662224000596-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140549926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}