SSRN Electronic Journal最新文献_第8页

Artificial intelligence, natural language processing, and machine learning to enhance e-service quality on e-commerce platforms 人工智能、自然语言处理和机器学习提高电子商务平台的电子服务质量

SSRN Electronic Journal Pub Date : 2024-07-05 DOI: 10.2139/ssrn.4847952

N. Rane, Saurabh Choudhary, Jayesh Rane

引用次数: 0

Moving beyond Beyond Budgeting: A case study of the dynamic interrelationships between budgets and forecasts 超越预算编制：预算与预测之间动态相互关系的案例研究

SSRN Electronic Journal Pub Date : 2024-07-04 DOI: 10.2139/ssrn.4846931

P. N. Bukh, Amalie Ringgaard, Niels Sandalgaard

引用次数: 0

Ensemble Deep Learning and Machine Learning: Applications, Opportunities, Challenges, and Future Directions 集合深度学习和机器学习：应用、机遇、挑战和未来方向

SSRN Electronic Journal Pub Date : 2024-07-04 DOI: 10.2139/ssrn.4849885

N. Rane, Saurabh Choudhary, Jayesh Rane

{"title":"Ensemble Deep Learning and Machine Learning: Applications, Opportunities, Challenges, and Future Directions","authors":"N. Rane, Saurabh Choudhary, Jayesh Rane","doi":"10.2139/ssrn.4849885","DOIUrl":"https://doi.org/10.2139/ssrn.4849885","url":null,"abstract":"The convergence of ensemble deep learning and machine learning has become a critical strategy for tackling intricate challenges across diverse fields such as healthcare, finance, and autonomous systems. Ensemble approaches, which combine the strengths of multiple models, are known for enhancing predictive accuracy, robustness, and generalizability. This paper investigates the applications of ensemble techniques, emphasizing their role in improving diagnostic precision in medical imaging, advancing fraud detection mechanisms in financial services, and refining decision-making in autonomous vehicles. Recent advancements in ensemble methods, including stacking, boosting, and bagging, have shown to outperform single models in various contexts. However, several challenges accompany the opportunities offered by ensemble learning, such as high computational demands, issues with model interpretability, and the potential for overfitting. This study explores ways to address these challenges, including the creation of more efficient algorithms and the incorporation of explainable AI (XAI) frameworks to enhance transparency and user trust. Furthermore, we discuss the future impact of cutting-edge technologies like quantum computing and federated learning on the evolution of ensemble techniques. The future of ensemble deep learning and machine learning is set to be shaped by the proliferation of big data, advancements in computational hardware, and the need for real-time, scalable solutions. This paper provides an extensive review of the current state of ensemble learning, identifies significant challenges, and suggests future research directions to fully harness the potential of these techniques in addressing real-world problems.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141679167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wages and Employment in the Netherlands, 2017-2023 2017-2023 年荷兰的工资和就业情况

SSRN Electronic Journal Pub Date : 2024-07-04 DOI: 10.2139/ssrn.4856928

Iris Klinker, B. ter Weel

引用次数: 0

Show Your Hand: The Impacts of Fair Pricing Requirements in Procurement Contracting 伸出你的手采购合同中公平定价要求的影响

SSRN Electronic Journal Pub Date : 2024-07-03 DOI: 10.2139/ssrn.4849021

Brad Nathan

引用次数: 0

The Effect of Securities Litigation Risk on Firm Value and Disclosure 证券诉讼风险对公司价值和信息披露的影响

SSRN Electronic Journal Pub Date : 2024-07-02 DOI: 10.2139/ssrn.4748971

D. Donelson, Christian M. Hutzler, Brian R. Monsen, Christopher G. Yust

引用次数: 0

The UK productivity puzzle: A survey of the literature and expert views 英国生产力之谜：文献和专家观点调查

SSRN Electronic Journal Pub Date : 2024-07-02 DOI: 10.2139/ssrn.4708301

Sam Williams, Anthony Glass, Madeleine Matos, Tom Elder, David Arnett

引用次数: 0

Assessing the impact of Industry 4.0 technologies on the social sustainability of agrifood companies 评估工业 4.0 技术对农业食品公司社会可持续性的影响

SSRN Electronic Journal Pub Date : 2024-07-02 DOI: 10.2139/ssrn.4874469

L. Cricelli, R. Mauriello, Serena Strazzullo, Mark Camilleri

引用次数: 0

Indian Stock Market Prediction using Augmented Financial Intelligence ML 利用增强型金融智能 ML 预测印度股市

SSRN Electronic Journal Pub Date : 2024-07-02 DOI: 10.2139/ssrn.4697853

Anishka Chauhan, Pratham Mayur, Y. Gokarakonda, Pooriya Jamie, Naman Mehrotra

{"title":"Indian Stock Market Prediction using Augmented Financial Intelligence ML","authors":"Anishka Chauhan, Pratham Mayur, Y. Gokarakonda, Pooriya Jamie, Naman Mehrotra","doi":"10.2139/ssrn.4697853","DOIUrl":"https://doi.org/10.2139/ssrn.4697853","url":null,"abstract":"This paper presents price prediction models using Machine Learning algorithms augmented with Superforecasters predictions, aimed at enhancing investment decisions. Five Machine Learning models are built, including Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU, and a model built using LSTM and GRU algorithms. The models are evaluated using the Mean Absolute Error to determine their predictive accuracy. Additionally, the paper suggests incorporating human intelligence by identifying Superforecasters and tracking their predictions to anticipate unpredictable shifts or changes in stock prices . The predictions made by these users can further enhance the accuracy of stock price predictions when combined with Machine Learning and Natural Language Processing techniques. Predicting the price of any commodity can be a significant task but predicting the price of a stock in the stock market deals with much more uncertainty. Recognising the limited knowledge and exposure to stocks among certain investors, this paper proposes price prediction models using Machine Learning algorithms. In this work, five Machine learning models are built using Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU and the last one is built using LSTM and GRU algorithms. Later these models are assessed using MAE scores to find which model is predicting with the highest accuracy. In addition to this, this paper also suggests the use of human intelligence to closely predict the shift in price patterns in the stock market The main goal is to identify Superforecasters and track their predictions to anticipate unpredictable shifts or changes in stock prices. By leveraging the combined power of Machine Learning and the Human Intelligence, predictive accuracy can be significantly increased.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Climate Change Competency Assessment: Focus on Lower Order Thinking Skills (LOTS) 气候变化能力评估：关注低阶思维能力 (LOTS)

SSRN Electronic Journal Pub Date : 2024-07-02 DOI: 10.2139/ssrn.4793998

Perzeus Lhey D. Villahermosa, July M. Villaren

{"title":"Climate Change Competency Assessment: Focus on Lower Order Thinking Skills (LOTS)","authors":"Perzeus Lhey D. Villahermosa, July M. Villaren","doi":"10.2139/ssrn.4793998","DOIUrl":"https://doi.org/10.2139/ssrn.4793998","url":null,"abstract":"In light of the increasingly dire consequences of climate change, fostering environmental literacy within the educational system has become an imperative. By measuring student competency, we can ensure future generations are empowered to become responsible stewards of the environment and active participants in tackling this global challenge. Thus, this study aims to assess the science students’ competency in factors that affect climate, the effects of changing climate, and how to adapt accordingly. The subjects of this study were thirty-one (31) science students, third-year and fourth-year students, composed of 8 males and 23 females who have taken the subject Environmental Science. Results show that students have a higher level of knowledge in competencies 1 and 2, namely: (1) Relate species extinction to the failure of the populations of organisms to adapt to abrupt changes in the environment, and (2) Explain how different factors affect the climate of an area. However, the present investigation yielded no statistically significant differences in climate change competency based on sex or year level, suggesting a potentially homogenous knowledge base regarding climate change across the studied demographic.","PeriodicalId":21855,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0