Machine learning for architectural design: Practices and infrastructure

2018 ◽  
Vol 16 (2) ◽  
pp. 123-143 ◽  
Author(s):  
Martin Tamke ◽  
Paul Nicholas ◽  
Mateusz Zwierzycki
Keyword(s):  
Machine Learning ◽  
Architectural Design ◽  
Performance Based Design ◽  
Future Research ◽  
Learning Approaches ◽  
Design Practice ◽  
Widespread Application ◽  
Design Practices ◽  
Architectural Research ◽  
The Impact

In this article, we propose that new architectural design practices might be based on machine learning approaches to better leverage data-rich environments and workflows. Through reference to recent architectural research, we describe how the application of machine learning can occur throughout the design and fabrication process, to develop varied relations between design, performance and learning. The impact of machine learning on architectural practices with performance-based design and fabrication is assessed in two cases by the authors. We then summarise what we perceive as current limits to a more widespread application and conclude by providing an outlook and direction for future research for machine learning in architectural design practice.

scholarly journals A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2764
Author(s):  
Xin Yu Liew ◽  
Nazia Hameed ◽  
Jeremie Clos
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Main Process ◽  
Learning Approaches ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Computer Aided ◽  
Conventional Methods ◽  
The Impact

A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.

An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Fathima Aliyar Vellameeran ◽  
Thomas Brindha
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Research Papers ◽  
Using Data ◽  
Intelligent Methods

Abstract Objectives To make a clear literature review on state-of-the-art heart disease prediction models. Methods It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed. Results The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions. Conclusions The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.

Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity

2019 ◽  
Vol 30 (1) ◽  
pp. 61-79 ◽  
Author(s):  
Weiyu Wang ◽  
Keng Siau
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Future Research ◽  
Way Of Life ◽  
Negative Effects ◽  
Future Research Directions ◽  
Negative Impacts ◽  
The Impact ◽  
The Way

The exponential advancement in artificial intelligence (AI), machine learning, robotics, and automation are rapidly transforming industries and societies across the world. The way we work, the way we live, and the way we interact with others are expected to be transformed at a speed and scale beyond anything we have observed in human history. This new industrial revolution is expected, on one hand, to enhance and improve our lives and societies. On the other hand, it has the potential to cause major upheavals in our way of life and our societal norms. The window of opportunity to understand the impact of these technologies and to preempt their negative effects is closing rapidly. Humanity needs to be proactive, rather than reactive, in managing this new industrial revolution. This article looks at the promises, challenges, and future research directions of these transformative technologies. Not only are the technological aspects investigated, but behavioral, societal, policy, and governance issues are reviewed as well. This research contributes to the ongoing discussions and debates about AI, automation, machine learning, and robotics. It is hoped that this article will heighten awareness of the importance of understanding these disruptive technologies as a basis for formulating policies and regulations that can maximize the benefits of these advancements for humanity and, at the same time, curtail potential dangers and negative impacts.

scholarly journals Impact Tech Startups: A Conceptual Framework, Machine-Learning-Based Methodology and Future Research Directions

2021 ◽  
Vol 13 (18) ◽  
pp. 10048
Author(s):  
Benjamin Gidron ◽  
Yael Israel-Cohen ◽  
Kfir Bar ◽  
Dalia Silberstein ◽  
Michael Lustig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Conceptual Framework ◽  
Impact Category ◽  
Private Investment ◽  
Social Enterprises ◽  
Future Research ◽  
Research Directions ◽  
For Profit ◽  
Future Research Directions ◽  
The Impact

The Impact Tech Startup (ITS) is a new, rapidly developing type of organizational category. Based on an entrepreneurial approach and technological foundations, ITSs adopt innovative strategies to tackle a variety of social and environmental challenges within a for-profit framework and are usually backed by private investment. This new organizational category is thus far not discussed in the academic literature. The paper first provides a conceptual framework for studying this organizational category, as a combination of aspects of social enterprises and startup businesses. It then proposes a machine learning (ML)-based algorithm to identify ITSs within startup databases. The UN’s Sustainable Development Goals (SDGs) are used as a referential framework for characterizing ITSs, with indicators relating to those 17 goals that qualify a startup for inclusion in the impact category. The paper concludes by discussing future research directions in studying ITSs as a distinct organizational category through the usage of the ML methodology.

scholarly journals Studying the Effect of Taking Statins before Infection in the Severity Reduction of COVID-19 with Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Alireza Davoudi ◽  
Mohsen Ahmadi ◽  
Abbas Sharifi ◽  
Roshina Hassantabar ◽  
Narges Najafi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diastolic Pressure ◽  
Systolic Pressure ◽  
Illness Severity ◽  
Support Vector ◽  
Learning Approaches ◽  
Angiotensin Converting Enzyme 2 ◽  
Decision Tree Method ◽  
The Impact

Statins can help COVID-19 patients’ treatment because of their involvement in angiotensin-converting enzyme-2. The main objective of this study is to evaluate the impact of statins on COVID-19 severity for people who have been taking statins before COVID-19 infection. The examined research patients include people that had taken three types of statins consisting of Atorvastatin, Simvastatin, and Rosuvastatin. The case study includes 561 patients admitted to the Razi Hospital in Ghaemshahr, Iran, during February and March 2020. The illness severity was encoded based on the respiratory rate, oxygen saturation, systolic pressure, and diastolic pressure in five categories: mild, medium, severe, critical, and death. Since 69.23% of participants were in mild severity condition, the results showed the positive effect of Simvastatin on COVID-19 severity for people that take Simvastatin before being infected by the COVID-19 virus. Also, systolic pressure for this case study is 137.31, which is higher than that of the total patients. Another result of this study is that Simvastatin takers have an average of 95.77 mmHg O2Sat; however, the O2Sat is 92.42, which is medium severity for evaluating the entire case study. In the rest of this paper, we used machine learning approaches to diagnose COVID-19 patients’ severity based on clinical features. Results indicated that the decision tree method could predict patients’ illness severity with 87.9% accuracy. Other methods, including the K -nearest neighbors (KNN) algorithm, support vector machine (SVM), Naïve Bayes classifier, and discriminant analysis, showed accuracy levels of 80%, 68.8%, 61.1%, and 85.1%, respectively.

scholarly journals Iterative machine learning applied to annotation of text datasets

2021 ◽  
Author(s):  
Thiago Abdo ◽  
Fabiano Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Computational Cost ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Learning Techniques ◽  
The Creation ◽  
The Impact ◽  
High Computational Cost

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

scholarly journals Evolution of corporate reputation during an evolving controversy

2019 ◽  
Vol 23 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Siyoung Chung ◽  
Mark Chong ◽  
Jie Sheng Chua ◽  
Jin Cheon Na
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Corporate Reputation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Content Type ◽  
Twitter Users ◽  
Corporate Crisis ◽  
The Impact

PurposeThe purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.Design/methodology/approachUsing a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.FindingsThe findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.Research limitations/implicationsEven with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.Practical implicationsFirst, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.Originality/valueThis study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

scholarly journals Machine Learning Approaches for Credit Card Fraud Detection

2018 ◽  
Vol 7 (2) ◽  
pp. 917
Author(s):  
S Venkata Suryanarayana ◽  
G N. Balaji ◽  
G Venkateswara Rao
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Language Processing ◽  
Credit Card ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Credit Card Fraud ◽  
Public Data ◽  
The Impact

With the extensive use of credit cards, fraud appears as a major issue in the credit card business. It is hard to have some figures on the impact of fraud, since companies and banks do not like to disclose the amount of losses due to frauds. At the same time, public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. Another problem in credit-card fraud loss estimation is that we can measure the loss of only those frauds that have been detected, and it is not possible to assess the size of unreported/undetected frauds. Fraud patterns are changing rapidly where fraud detection needs to be re-evaluated from a reactive to a proactive approach. In recent years, machine learning has gained lot of popularity in image analysis, natural language processing and speech recognition. In this regard, implementation of efficient fraud detection algorithms using machine-learning techniques is key for reducing these losses, and to assist fraud investigators. In this paper logistic regression, based machine learning approach is utilized to detect credit card fraud. The results show logistic regression based approaches outperforms with the highest accuracy and it can be effectively used for fraud investigators.  

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