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This study explores whether a new machine learning method can more accurately predict the movement of stock prices.

This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.

The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.

This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

Cryptocurrency markets are gaining popularity, with over 23,000 cryptocurrencies in 2023 and a total market valuation of 870.81 billion USD in 2023. With its increasing popularity, cryptocurrencies are also susceptible to volatility. Predicting the price with the least fallacy or more accuracy has become the need of the hour as it significantly influences investment decisions.

This study aims to create a dynamic forecasting model using the ensemble method and test the forecasting accuracy of top 15 cryptocurrencies’ prices. Statistical and econometric model prediction accuracy is examined after hyper tuning the parameters. Drawing inferences from the statistical model, an ensemble model using machine learning (ML) algorithms is developed using gradient-boosted regressor (GBR), random forest regressor (RFR), support vector regression (SVR) and multi-layer perceptron (MLP). Validation curves are utilized to optimize model parameters and boost prediction accuracy.

It is found that when the price movement exhibits autocorrelation, the autoregressive integrated moving average (ARIMA) model and the ensemble model performed better. ARIMA, simple linear regression (SLR), random forest (RF), decision tree (DT), gradient boosting (GB) and multi-model regression (MLR) ensemble models performed well with coins, showing that trends, seasonality and historical price patterns are prominent. Furthermore, the MLR approach produces more accurate predictions for coins with higher volatility and irregular price patterns.

Although the dataset includes crisis period data, anomalies or outliers are yet to be explicitly excluded from the analysis. The models employed in this study still demonstrate high accuracy in predicting cryptocurrency prices despite these outliers, suggesting that the models are robust enough to handle unexpected fluctuations or extreme events in the market. However, the lack of specific analysis on the impact of outliers on model performance is a limitation of the study, as it needs to fully explore the resilience of the forecasting models under adverse market conditions.

The present study contributes to the body of literature on ensemble methods in forecasting crypto price in general, potentially influencing future studies on price forecasting. The study motivates the researchers on empirical testing of our framework on various asset classes. As a result, on the prediction ability of ensemble model, the study will significantly influence the decision-making process of traders and investors. The research benefits the traders and investors to effectively develop a model to forecast cryptocurrency price. The findings highlight the potential of ensemble model in predicting high volatile cryptocurrencies and other financial assets. Investors can design the investment strategies and asset allocation decisions by understanding the relationship between market trends and consumer behavior. Investors can enhance portfolio performance and mitigate risk by incorporating these insights into their decision-making processes. Policymakers can use this information to design more effective regulations and policies promoting economic stability and consumer welfare. The study emphasizes the need for using diversified model to understand the market dynamics and improving trading strategies.

This research, to the best of our knowledge, is the first to use the above models to develop an ensemble model on the data for which the outliers have not been adjusted, and the model still outperformed the other statistical, econometric, ML and deep learning (DL) models.

加密貨幣市場越來越受歡迎; 於2023年,不同種類的加密貨幣為數已超過23,000種; 同年,它們的總市場估值為八千七百零八點壹億美元。雖然加密貨幣越來越受歡迎,但它們仍然容易受到波動性的影響。預測謬誤減至最少的價格或作出更準確的價格預測就成為某些特定時刻的首要事項,這是因為投資決策會顯著地受到這些預測的影響。

研究人員擬以集成學習方法來創造一個動態預測模型,並以此模型測試預測15個頂尖加密貨幣價格的準確性。 研究人員調校超參數後,便審查統計及計量經濟學模式的預測準確性。研究人員基於從統計模式作出的推斷,研製一個使用機器學習算法的集成模型。研究人員在研製這個集成模型時,使用了梯度提升迴歸變量、隨機森林迴歸、支持向量迴歸和多層感知器。 驗證曲線被用來優化模型參數,以及提高預測的準確性。

研究人員發現,當價格變動展示自相關時,差分整合移動平均自我迴歸模型和集成模型會表現得更好; 另外,若使用加密貨幣,差分整合移動平均自我迴歸模型、簡單線性迴歸、隨機森林、決策樹、梯度提升和多模型迴歸集成模型會有良好的表現。再者,就波動性較高和價格模式不規則的加密貨幣而言,採用多重線性迴歸的方法會使預測更為準確。

據我們所知,這是首個研究,以上述的各個模型來研發一個集成模型,而這個集成模型,雖建基於異常值並未調整的數據,但它的表現卻比其它的統計、計量經濟學、多重線性和深度學習等的模型更為優良。

This study examines innovation configurations (i.e. sets of product/service, social and business model innovations) and configuration linkages (i.e. factors that help to combine innovations) across six organizations as contingent upon organizational structure.

Using semi-structured interviews and available public information, qualitative data were collected and examined using content analysis to characterize innovation configurations and linkages in three local/private organizations and three foreign-led/public-private partnerships in Repiblik Ayiti (Haiti).

Organizations tend to combine product/service, social, and business model innovations simultaneously in locally founded private organizations and sequentially in foreign-based public-private partnerships. Linkages for simultaneous combination include limited external support, determined autonomy and shifting from a “beneficiary mindset,” and financial need identification. Sequential combination linkages include social need identification, community connections and flexibility.

The generalizability of our findings for this qualitative study is subject to additional quantitative studies to empirically test the suggested factors and to examine other health care organizations and countries.

Locally led private organizations in low- and middle-income settings may benefit from considering how their innovations are in service to one another as they may have limited resources. Foreign based public-private partnerships may benefit from pacing their efforts alongside a broader set of stakeholders and ecosystem partners.

This study is the first, to our knowledge, to examine how organizations combine sets of innovations, i.e. innovation configurations, in a healthcare setting and the first of any setting to examine innovation configuration linkages.

In the context of Portugal, this study examines the stereotypes of accountants held by laypeople and how they are influenced by financial crises and accounting scandals.

To better understand the social images of accountants, the authors adopt a structural approach based on the big five model (BFM) of personality. The authors test this approach on a Portuguese community sample (N = 727) using a questionnaire survey. The results are analyzed considering the socioanalytic theory.

The results suggest the existence of a stereotype dominated by features of conscientiousness, which is related to the superior performance of work tasks across job types. This feature comprises the core characteristics of the traditional accountant stereotype, which survives in a context challenged by financial scandals and crises. The findings highlight the social acceptance of accountants as an occupational group but do not suggest the possibility of accountants benefiting from the highest levels of social status when considered in relation to the traditional accountant stereotype.

By combining the BFM and the socioanalytic theory, this study provides a unique theoretical approach to better understand the social images of accountants. The findings demonstrate the suitability of using the BFM to study the social perceptions of accountants. They also indicate a paradox based on the survival of the traditional stereotype. This stereotype appears to be resistant to scandals and financial crisis, instead of being impaired, giving rise to another prototype with concerns about integrity.

The purpose of the study is to identify the strengths, weaknesses, opportunities and threats in higher education regarding distance learning system in Pakistan.

A mixed-method research design was used in this study. The population of the study was all the previous students of research work and all the teachers of these students working at MPhil and PhD level in AIOU in Pakistan. Stratified random sampling technique was used in this study. This study used the questionnaire and interview technique to collect data. Data of questionnaire was in numbers and data of interview was narrative. So it was the need of the study that a mixed-method approach, i.e. both quantitative and qualitative techniques should be used in this study.

The findings of the study show that AIOU has also strengths, weaknesses, opportunities and threats in higher education related to distance education like all the formal and distance universities of Pakistan and World. This study reflected that AIOU is a great blessing for those who cannot acquire their education regularly because of financial/family problems or they are job holders. But this study also described that at higher level students are facing a lot of problems especially there is a delay in research process and provision of no scholarships to students. The teachers have a low salary package than the other public universities of Pakistan and a lot of responsibilities to attend meetings, seminars, conferences and workshops. So they have less time for research work. And AIOU provides them fewer opportunities to go abroad for further studies or to attend conferences/seminars. This study recommended that there should be adopted such policies in AIOU that students could get their study materials, assignments duration, workshops schedule and degrees in time, the teachers of the concerned departments should allow to take more in numbers the students under their supervision, the pay package for the teachers working at MPhil and PhD level should be raised and the opportunities for the faculty members to go.

This study is limited to analyze the higher education system especially the distance learning system in Pakistan.

This study has indicated the strengths, weaknesses, opportunities and threats in higher education which the AIOU is facing recently and the policymakers can develop plans/strategies to make better the distance learning system especially at higher level in Pakistan. This study can be helpful for the stakeholders who are interested in distance learning system. This study was conducted at higher level in the distance learning system but it can open the ways for the other researchers to conduct research in other disciplines related to distance education, i.e. at matric level, F.A/F.SC, B.Sc programs, Master level and M.Sc programs at AIOU.

Through this study, it can be acknowledged how the AIOU is providing the opportunity of education to a large number of people in the society who cannot study regularly in the formal institutions especially those who are job holders, some financial problems and women who have some family problems and above one million people are benefitting from AIOU in Pakistan and world.

This study is original in this respect because the data has been collected from the participants, i.e. students and teachers of AIOU. And it has also great value because this is the first SWOT analysis which has been conducted in this university to examine the strengths, weaknesses, opportunities and threats facing AIOU at present time. This study can also become a base for the stakeholders', i.e. policymakers, administration and higher education depart. of Pakistan in developing strategies to improve and amend the distance learning system of Pakistan especially at higher level in AIOU.

The article discusses the current relevance of artificial intelligence (AI) in research and how AI improves various research methods. This article focuses on the practical case study of systematic literature reviews (SLRs) to provide a guideline for employing AI in the process.

Researchers no longer require technical skills to use AI in their research. The recent discussion about using Chat Generative Pre-trained Transformer (GPT), a chatbot by OpenAI, has reached the academic world and fueled heated debates about the future of academic research. Nevertheless, as the saying goes, AI will not replace our job; a human being using AI will. This editorial aims to provide an overview of the current state of using AI in research, highlighting recent trends and developments in the field.

The main result is guidelines for the use of AI in the scientific research process. The guidelines were developed for the literature review case but the authors believe the instructions provided can be adjusted to many fields of research, including but not limited to quantitative research, data qualification, research on unstructured data, qualitative data and even on many support functions and repetitive tasks.

AI already has the potential to make researchers’ work faster, more reliable and more convenient. The authors highlight the advantages and limitations of AI in the current time, which should be present in any research utilizing AI. Advantages include objectivity and repeatability in research processes that currently are subject to human error. The most substantial disadvantages lie in the architecture of current general-purpose models, which understanding is essential for using them in research. The authors will describe the most critical shortcomings without going into technical detail and suggest how to work with the shortcomings daily.