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This paper aims to examine potential diversification benefits between Eurozone (i.e. EURO STOXX 50) and key Asia markets: HSI (Hong Kong), KOSPI (South Korea), NIKKEI 225 (Japan) and TSEC (Taiwan). The sample covers the period from 04-01-2008 to 19-10-2023 in daily frequency.

The empirical investigation is based on the wavelet coherence analysis, which is a localized correlation coefficient in the time and frequency domain.

The results provide evidence that long-term diversification benefits exist between EURO STOXX and NIKKEI, EURO STOXX and KOSPI (after 2015) and there are signs for the pair and EURO STOXX-TSEC (after 2014). During the short term, there are signs of diversification benefits during the sample period. However, during the medium term, the diversification benefits seem to diminish.

These results have crucial implications for investors regarding the benefits of international portfolio diversification.

The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.

A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.

The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.

This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.

Feature extraction from 3D datasets is a current problem. Machine learning is an important tool for classification of complex 3D datasets. Machine learning classification techniques are widely used in various fields, such as text classification, pattern recognition, medical disease analysis, etc. The aim of this study is to apply the most popular classification and regression methods to determine the best classification and regression method based on the geodesics.

The feature vector is determined by the unit normal vector and the unit principal vector at each point of the 3D surface along with the point coordinates themselves. Moreover, different examples are compared according to the classification methods in terms of accuracy and the regression algorithms in terms of R-squared value.

Several surface examples are analyzed for the feature vector using classification (31 methods) and regression (23 methods) machine learning algorithms. In addition, two ensemble methods XGBoost and LightGBM are used for classification and regression. Also, the scores for each surface example are compared.

To the best of the author’s knowledge, this is the first study to analyze datasets based on geodesics using machine learning algorithms for classification and regression.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

This paper aims to analyze the dependence structure between the Green Sukuk Spread in Malaysia and uncertainty factors from January 1, 2017, to May 23, 2023, covering two main periods: the pre-COVID-19 and the COVID-19 periods.

This study contributes to the current literature by explicitly modeling nonlinear dependencies using the Regular vine copula approach to capture asymmetric characteristics of the tail dependence distribution. This study used the Archimedean copula models: Student’s-t, Gumbel, Gaussian, Clayton, Frank and Joe, which exhibit different tail dependence structures.

The empirical results suggest that Green Sukuk and various uncertainty variables have the strongest co-dependency before and during the COVID-19 crisis. Due to external uncertainties (COVID-19), the results reveal that global factors, such as the Infect-EMV-index and the higher financial stress index, significantly affect the spread of Green Sukuk. Interestingly, in times of COVID-19, its dependence on Green Sukuk and the news sentiment seems to be a symmetric tail dependence with a Student’s-t copula. This result is relevant for hedging strategies, as investors can enhance the performance of their portfolio during the COVID-19 crash period.

This study contributes to a better understanding of the dependency structure between Green Sukuk and uncertainty factors. It is relevant for market participants seeking to improve their risk management for Green Sukuk.

The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.

316 L stainless steel alloy is potentially the most used material in the selective laser melting (SLM) process because of its versatility and broad fields of applications (e.g. medical devices, tooling, automotive, etc.). That is why producing fully functional parts through optimal printing configuration is still a key issue to be addressed. This paper aims to present an entirely new framework for simultaneously reducing surface roughness (SR) while increasing the material processing rate in the SLM process of 316L stainless steel, keeping fundamental mechanical properties within their allowable range.

Considering the nonlinear relationship between the printing parameters and features analyzed in the entire experimental space, machine learning and statistical modeling methods were defined to describe the behavior of the selected variables in the as-built conditions. First, the Box–Behnken design was adopted and corresponding experimental planning was conducted to measure the required variables. Second, the relationship between the laser power, scanning speed, hatch distance, layer thickness and selected responses was modeled using empirical methods. Subsequently, three heuristic algorithms (nonsorting genetic algorithm, multi-objective particle swarm optimization and cross-entropy method) were used and compared to search for the Pareto solutions of the formulated multi-objective problem.

A minimum SR value of approximately 12.83 μm and a maximum material processing rate of 2.35 mm³/s were achieved. Finally, some verification experiments recommended by the decision-making system implemented strongly confirmed the reliability of the proposed optimization methodology by providing the ultimate part qualities and their mechanical properties nearly identical to those defined in the literature, with only approximately 10% of error at the maximum.

To the best of the authors’ knowledge, this is the first study dealing with an entirely different and more comprehensive approach for optimizing the 316 L SLM process, embedding it in a unique framework of mechanical and surface properties and material processing rate.