Sensing the Future: A Design Framework for Context-Aware Predictive Systems

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Avital, Michel; Chatterjee, Samir; Furtak, Szymon

Abstract: Sensors embedded in smart objects, smart machines, and smart buildings produce ... Expand

Abstract: Sensors embedded in smart objects, smart machines, and smart buildings produce ever-growing streams of contextual data that convey information of interest about their operating environment. Although an increasing number of industries have embraced the utilization of sensors in routine operations, no clear framework is available to guide designers who aim to leverage contextual data collected from these sensors to develop predictive systems. In this paper, we applied design science research methodology to develop and evaluate a general framework that can help designers build predictive systems utilizing sensor data. Specifically, we developed a framework for designing context-aware predictive systems (CAPS). We then evaluated the framework through its application in MAN Diesel & Turbo, which served as a case company. The framework can be generalized into a class of demand-forecasting problems that rely on sensor-generated contextual data. The CAPS framework is unique and can help practitioners make better-informed decisions when designing context-aware predictive systems. Collapse

Topics: data quality evaluation criteria knowledge base systems design systems development

Methods: machine learning design science design artifact data transformation design process

Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction

2023 | Decision Support Systems | Citations: 0

Authors: Ben-Assuli, Ofir; Heart, Tsipi; Klempfner, Robert; Padman, Rema

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of a ... Expand

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of adverse medical events is important considering the prevalence of vast repositories of patient-level medical data, and the quest to reduce healthcare costs while elevating the quality of care. Of particular interest is identifying a set of features that can better predict the risk of unplanned readmissions of chronically ill patients within 30 days post discharge, among them patients with Congestive Heart Failure (CHF). While numerous studies have employed ML algorithms to identify sets of significant features, only a handful have compared features extracted by various methods, and even fewer have compared the predictive power of feature sets selected by human experts to those selected by ML. Hence, this research aimed to elicit a comprehensive feature set selected by four ML algorithms and compare its predictive performance to that of a feature set selected by experts. We then evaluated a merged list of the two feature sets, constructing a human-machine collaborative set to predict the likelihood of 30-day unplanned readmission of CHF patients using data on 10,763 CHF patients hospitalized during the years 2010–2017. Our models achieved Area under the ROC Curve (AUC) above 0.8 and an Accuracy of ~0.89, comparable to the best models in the extant literature. Moreover, the Human-Machine Collaborative model significantly outperformed (best AUC 0.836) other human-only selected models as well as the machine-selected set. This study contributes to a better understanding of the power of using ML for patient risk stratification with special attention to the benefits of human-machine collaboration, even when these two entities work separately or in parallel, for improved clinical decision making. Collapse

Topics: healthcare data decision making big data missing data data warehouse

Methods: machine learning computational algorithm XGBoost descriptive statistic random forest classification

Towards a thematic dimensional framework of online fraud: An exploration of fraudulent email attack tactics and intentions

2023 | Decision Support Systems | Citations: 1

Authors: Bera, Debalina; Ogbanufe, Obi; Kim, Dan J.

Abstract: Despite anti-phishing filters, social engineering-based cyber-attacks still resu ... Expand

Abstract: Despite anti-phishing filters, social engineering-based cyber-attacks still result in billions of dollars lost annually, significant personal identity theft, loss of corporate secrets, and espionage. We review the phishing literature on psychological attacks and design tactics employed for deception by attackers. The result reveals the need to continuously identify tactics embedded in fraudulent email content to understand why users still fall prey to phishing attacks. Using the literature as a backdrop, we identify and conceptualize a thematic dimensional framework of fraudulent phishing attacks. This study uses benchmark datasets of fraudulent email to empirically validate the proposed dimensional framework. Specifically, a combination of machine learning-based content analysis and topic modeling found a majority of the discovered topics were labeled against the proposed di­ mensions. Statistical analysis was employed to provide empirical evidence of the thematic dimensions of fraudulent email attacks. This study thus, not only extends the cybersecurity literature by identifying and vali­ dating eight dimensions that enrich our understanding of phishing and attack identification but stimulates cu­ mulative research endeavors to develop yet more comprehensive dimensional frameworks of phishing email attacks. Collapse

Topics: phishing email phishing electronic mail email spam personalization

Methods: term frequency–inverse document frequency natural language processing topic model discriminant analysis literature study

Great Divisions: The Evolution of Polarization During the Man-made Emergency of January 6, 2021

2023 | HICSS | Citations: 0

Authors: Bhatt, P.; Vemprala, N.; Valecha, R.; Hariharan, G.; Rao, H.

Abstract: Polarization, which refers to the formation of opposing groups based on peoples ... Expand

Abstract: Polarization, which refers to the formation of opposing groups based on peoples' beliefs and opinions, is a phenomenon that has existed for eons. However, in the recent past, social media has caused a quantum change in the dynamics of polarization since beliefs may change almost instantaneously on social media because of unfolding events. We investigate social media communication that has resulted in polarized opinions among individuals prior to, during, and after a crisis event; the January 6th US Capitol riots. Analyses of the dominant narratives on Twitter surrounding the incident reveal a high level of polarization throughout the unfolding of the event, with increased polarization possibly attributable to the onset of the crisis. We also observe the evolution of the phenomenon: as the event unfolds, polarization changes. We suggest that understanding the evolution of polarization is important for timely crisis resolution by crisis managers. We utilize three measures, spread, distinctness and regionalization, to examine polarization during a crisis. Based on these measures our findings indicate that social media discussions exhibit an increase in the negative sentiment during the crisis as compared to precrisis, but there is a rebound in terms of positive sentiments emerging in the post crisis period. Also, the results signal reduced diversity in discussion topics during the crisis as compared to pre and post-crisis. Collapse

Topics: social media Twitter terrorist incident artificial intelligence application programming interface

Methods: topic model longitudinal research qualitative content analysis longitudinal survey cluster analysis

Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Fechner, Pascal; König, Fabian; Kratsch, Wolfgang; Lockl, Jannik; Röglinger, Maximilian

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of th ... Expand

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of their bladder filling. To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary, which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world in vivo dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients. Collapse

Topics: computer hardware open source mobile application user interface programming language

Methods: design artifact qualitative interview design process support vector machine computational algorithm

Co-evolution of neural architectures and features for stock market forecasting: A multi-objective decision perspective

2023 | Decision Support Systems | Citations: 0

Authors: Hafiz, Faizal; Broekaert, Jan; La Torre, Davide; Swain, Akshya

Abstract: In a multi-objective setting, a portfolio manager’s highly consequential decisio ... Expand

Abstract: In a multi-objective setting, a portfolio manager’s highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to identify a set of non-dominated neural network models for further selection by the decision-maker. A new coevolution approach is proposed to simultaneously select the features and topology of neural networks (collectively referred to as neural architecture), where the features are viewed from a topological perspective as input neurons. Further, the co-evolution is posed as a multi-criteria problem to evolve sparse and efficacious neural architectures. The well-known dominance and decomposition based multi-objective evolutionary algorithms are augmented with a non-geometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the co-evolution is augmented to accommodate the data-based implications of distinct market behaviors prior to and during the ongoing COVID-19 pandemic. A detailed comparative evaluation is carried out with the conventional sequential approach of feature selection followed by neural topology design, as well as a scalarized co-evolution approach. The results on three market indices (NASDAQ, NYSE, and S&P500) in pre- and peri-COVID time windows convincingly demonstrate that the proposed co-evolution approach can evolve a set of non-dominated neural forecasting models with better generalization capabilities. Collapse

Topics: covid 19 pandemic decision support system decision making

Methods: computational algorithm artificial neural network data transformation machine learning principal component analysis

Theories: co-evolution theory

FROM EMERGENCY REMOTE ASSESSMENT TO A NEW STATUS QUO? – LESSONS LEARNED FROM ONLINE ASSESSMENTS DURING THE COVID-19 PANDEMIC

2023 | European Conference On Information Systems | Citations: 0

Authors: Hartmann, Philipp; Hobert, Sebastian

Abstract: Before the COVID-19 pandemic, studying at German universities was traditionally ... Expand

Abstract: Before the COVID-19 pandemic, studying at German universities was traditionally dominated by inclass activities. During the pandemic, technical and organizational foundations were laid not only for teaching but also for exams. With the resumption of in-class events, the question is to what extent online exams will also be used in the future. Here, examiners and educational institutions can learn from the experience during the pandemic to increase the students' intention to participate in online exams in the future. To identify the problems encountered and derive lessons from them, a mixed-method approach was followed. For this purpose, 478 examinees were surveyed over 3 semesters using a quantitative questionnaire, followed by in-depth semi-structured follow-up interviews with 11 students. As a result, 5 recommendations for action could be derived, covering the topics of exam routine and familiarity, problem-solving, cheating, and teaching-examination arrangements. Collapse

Topics: computer based testing pandemic knowledge type knowledge sharing

Methods: survey descriptive statistic personal interview data transformation design principle

IoT and Wearable Devices-Enhanced Information Provision of AR Glasses: A Multi-Modal Analysis in Aviation Industry

2023 | International Conference on Information Systems | Citations: 0

Authors: Han, Xuewen; Li, Ting; Xu, Sean Xin; Yang, Zherui; Yin, Zhitao; Zhang, Kunpeng

Abstract: While Augmented Reality (AR) glasses are now instrumental in industries for del ... Expand

Abstract: While Augmented Reality (AR) glasses are now instrumental in industries for delivering work-related information, the current one-size-fits-all information provision of AR glasses fails to cater to diverse workers' needs and environmental conditions. We propose a framework for harnessing Internet of thing (IoT) and wearable technology to improve the adaptability and customization of information provision by AR. As a preliminary exploration, this short paper develops a multi-modal data processing system for work performance classification in the aviation industry. Using machine learning algorithms for multi-modal feature extraction and classifier construction, this framework provides a more objective and consistent evaluation of work performance compared to singlemodal approaches. The proposed analytics architecture can provide valuable insights for other industries struggling to implement IoT and mixed reality. Collapse

Topics: smart wearable device mobile system internet of things individual characteristic ergonomics

Methods: oculometry machine learning support vector machine lasso regression robustness check

The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge

2023 | Information Systems Frontiers | Citations: 0

Authors: Harfouche, Antoine; Quinio, Bernard; Saba, Mario; Saba, Peter Bou

Abstract: Artificial intelligence (AI) has increased the ability of organizations to accum ... Expand

Abstract: Artificial intelligence (AI) has increased the ability of organizations to accumulate tacit and explicit knowledge to inform management decision-making. Despite the hype and popularity of AI, there is a noticeable scarcity of research focusing on AI's potential role in enriching and augmenting organizational knowledge. This paper develops a recursive theory of knowledge augmentation in organizations (the KAM model) based on a synthesis of extant literature and a four-year revised canonical action research project. The project aimed to design and implement a human-centric AI (called Project) to solve the lack of integration of tacit and explicit knowledge in a scientific research center (SRC). To explore the patterns of knowledge augmentation in organizations, this study extends Nonaka's SECI (socialization, externalization, combination, and internalization) model by incorporating the human-in-the-loop Informed Artificial Intelligence (IAI) approach. The proposed design offers the possibility to integrate experts' intuition and domain knowledge in AI in an explainable way. The findings show that organizational knowledge can be augmented through a recursive process enabled by the design and implementation of human-in-the-loop IAI. The study has important implications for research and practice. Collapse

Topics: artificial intelligence organizational memory explicit knowledge human in the loop human computer interaction

Methods: computational algorithm action research qualitative interview participant observation statistical hypothesis test

Theories: SECI model of knowledge dimensions organizational knowledge creation

Can Conversations on Reddit Forecast Future Economic Uncertainty? An Interpretable Machine Learning Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Jiang, Jinhang; Feng, Mei; Srinivasan, Karthik

Abstract: In recent years, social media has become an indispensable source of information ... Expand

Abstract: In recent years, social media has become an indispensable source of information through which public attitudes, opinions, and concerns can be studied and quantified. This paper proposes an interpretable machine learning framework for predicting the Equity Market-related Economic Uncertainty Index using features generated from a popular discussion forum on Reddit. Our framework consists of a series of custom preprocessing and analytics methods, including BERTopic for latent topic identification and regularized linear models. Using our framework, we evaluate explanatory models with different configurations over a large corpus of Reddit posts belonging to the personal finance category. Our analysis generates valuable insights about discussion topics on Reddit and their efficacy in accurately predicting future economic uncertainty. The study demonstrates the potential of using social media data and interpretable machine learning to inform economic forecasting research. Collapse

Topics: Reddit social media explainable artificial intelligence Python permutation feature importance

Methods: descriptive statistic time series analysis longitudinal research topic model experiment