Black-box error diagnosis in Deep Neural Networks for computer vision: a survey of tools

Fraternali, Piero; Milani, Federico; Torres, Rocio Nahime; Niccolò, Zangrando,

doi:10.1007/s00521-022-08100-9

“…However, the complex and opaque nature of these architectures often categorizes them as "black boxes", making it challenging to understand their internal workings and decision-making processes. Fraternali et al (2022) highlight the necessity of methods that go beyond standard performance metrics to diagnose model behavior and prediction errors, thereby enhancing our understanding of DNNs. The black-box nature of Artificial Neural Networks (ANNs) raises ethical and forensic concerns, especially in mission-critical applications affecting human life, such as healthcare and finance.…”

Section: Neural Network: Unveiling the Black Boxmentioning

confidence: 99%

Predicting Stock Market Movements Using Neural Networks: A Review and Application Study

Adedoyin Tolulope Oyewole,

Omotayo Bukola Adeoye,

Wilhelmina Afua Addy

et al. 2024

Comput. sci. IT res. j.

6

0

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In the rapidly evolving landscape of financial markets, the quest for accurate stock market predictions has never been more critical. This paper delves into the transformative potential of neural network models in forecasting stock market movements, offering a comprehensive examination of their effectiveness compared to traditional predictive models. With a focus on the evolution of stock market prediction methodologies, this study aims to uncover the nuanced dynamics of neural networks, their comparative analysis with other models, and the pivotal role of data preprocessing in enhancing prediction accuracy. Employing a qualitative analysis framework, the research meticulously synthesizes findings from selected studies, highlighting the superior performance of neural network models in capturing complex market patterns and adapting to volatility. The results underscore the significant impact of data quality and quantity, architectural nuances of neural networks, and the strategic implications for investors navigating the stock market's unpredictability. Despite the promising outcomes, the study acknowledges inherent challenges in the real-world application of these models, including data imperfections and the complexity of financial ecosystems. Conclusively, the paper advocates for ongoing innovation, interdisciplinary collaboration, and the strategic integration of advanced neural network architectures to overcome existing limitations. Recommendations emphasize the critical need for high-quality, diverse datasets and continuous model refinement to harness the full predictive power of neural networks in stock market forecasting. This study not only illuminates the path forward for investors and financial analysts but also sets the stage for future research in this dynamic field. Keywords: Neural Network Models, Stock Market Prediction, Data Preprocessing, Model Accuracy, Financial Markets, Innovation.

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“…However, the complex and opaque nature of these architectures often categorizes them as "black boxes", making it challenging to understand their internal workings and decision-making processes. Fraternali et al (2022) highlight the necessity of methods that go beyond standard performance metrics to diagnose model behavior and prediction errors, thereby enhancing our understanding of DNNs.…”

Section: Neural Network: Unveiling the Black Boxmentioning

confidence: 99%

Predicting stock market movements using neural networks: A review and application study

Olatunji Akinrinola,

Wilhelmina Afua Addy,

Adeola Olusola Ajayi-Nifise

et al. 2024

GSC Adv. Res. Rev.

1

0

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In the rapidly evolving landscape of financial markets, the quest for accurate stock market predictions has never been more critical. This paper delves into the transformative potential of neural network models in forecasting stock market movements, offering a comprehensive examination of their effectiveness compared to traditional predictive models. With a focus on the evolution of stock market prediction methodologies, this study aims to uncover the nuanced dynamics of neural networks, their comparative analysis with other models, and the pivotal role of data preprocessing in enhancing prediction accuracy. Employing a qualitative analysis framework, the research meticulously synthesizes findings from selected studies, highlighting the superior performance of neural network models in capturing complex market patterns and adapting to volatility. The results underscore the significant impact of data quality and quantity, architectural nuances of neural networks, and the strategic implications for investors navigating the stock market's unpredictability. Despite the promising outcomes, the study acknowledges inherent challenges in the real-world application of these models, including data imperfections and the complexity of financial ecosystems. Conclusively, the paper advocates for ongoing innovation, interdisciplinary collaboration, and the strategic integration of advanced neural network architectures to overcome existing limitations. Recommendations emphasize the critical need for high-quality, diverse datasets and continuous model refinement to harness the full predictive power of neural networks in stock market forecasting. This study not only illuminates the path forward for investors and financial analysts but also sets the stage for future research in this dynamic field.

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“…Current DL models are plagued by information leakage and only provide limited measures of generalizability, the model's performance in real-world populations. The inherent 'black box' nature of neural networks impedes the interpretation of model functions and the subsequent feedback of knowledge for clinicians [91].…”

mentioning

confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

¹

,

Wang

²

,

Yu

³

et al. 2023

MAKE

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Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

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Black-box error diagnosis in Deep Neural Networks for computer vision: a survey of tools

Cited by 7 publications

References 73 publications

Predicting Stock Market Movements Using Neural Networks: A Review and Application Study

Predicting Stock Market Movements Using Neural Networks: A Review and Application Study

Predicting stock market movements using neural networks: A review and application study

A Survey of Deep Learning for Alzheimer’s Disease

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