Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities

2023 | Information Systems Frontiers | Citations: 0

Authors: Alahakoon, Damminda; Nawaratne, Rashmika; Xu, Yan; De Silva, Daswin; Sivarajah, Uthayasankar; Gupta, Bhumika

Abstract: The emerging information revolution makes it necessary to manage vast amounts of ... Expand

Abstract: The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the self-building AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications. Collapse

Topics: artificial intelligence smart city big data cloud computing distributed system

Methods: computational algorithm experiment unsupervised learning machine learning longitudinal research

Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews

2023 | International Journal of Information Management | Citations: 0

Authors: Joung, Junegak; Kim, Harrison

Abstract: For new product development, previous segmentation methods based on demographic, ... Expand

Abstract: For new product development, previous segmentation methods based on demographic, psychographic, and purchase behavior information cannot identify a group of customers with unsatisfied needs. Moreover, seg­ mentation is limited to sales promotions in marketing. Although needs-based segmentation considering customer sentiments on product features can be conducted to develop a new product concept, it cannot identify com­ monalities among customers owing to their diverse preferences. Therefore, this paper proposes an interpretable machine learning-based approach for customer segmentation for new product development based on the importance of product features from online product reviews. The technical challenges of determining the importance of product features in each review are identifying and interpreting the nonlinear relations between satisfaction with product features and overall customer satisfaction. In this study, interpretable machine learning is used to identify these nonlinear relations with high performance and transparency. A case study on a wearable device is conducted to validate the proposed approach. Customer segmentation using the proposed approach based on importance is compared with that employing a previous approach based on sentiments. The results show that the proposed approach presents a higher clustering performance than the previous approach and offers opportunities to identify new product concepts. Collapse

Topics: online review shapley additive explanation customer satisfaction new product development Python

Methods: cluster analysis machine learning sentiment analysis case study word2vec

Theories: game theory

Deep Learning-Based Imputation Method to Enhance Crowdsourced Data on Online Business Directory Platforms for Improved Services

2023 | Journal of Management Information Systems | Citations: 0

Authors: Xu, Da; Hu, Paul Jen-Hwa; Fang, Xiao

Abstract: Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, ... Expand

Abstract: Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, depend on voluntarily user-submitted data about various businesses to assist consumers in finding appropriate options for transactions. Yet the crowdsourced nature of such data restricts the availability of attribute values for many businesses on the platform. Crowdsourced data often suffer serious completeness and timeliness constraints, with negative implications for key stakeholders such as users, businesses, and the platform. We thus develop a novel, deep learning–based imputation method, premised in institutional theory, to estimate missing attribute values of individual businesses on an OBD platform. The proposed method leverages a deep model architecture and considers both inter-business and inter-attribute relationships for imputations. An application to a Yelp data set reveals our method's greater imputation effectiveness relative to prevalent methods. To illustrate the method's practical utilities and values, we further examine the efficacy of business recommendations empowered by its imputed business attribute values, in comparison with those enabled by data imputed by benchmark methods. The results affirm that the proposed method substantially outperforms benchmarks for imputing missing attribute values and empowers more effective business recommendations. This study addresses crucial, prominent completeness and timeliness constraints in crowdsourced data on OBD platforms and offers insights for downstream applications that can improve user experiences, firm performance, and platform services. Collapse

Topics: missing data crowdsourcing data completeness wireless LAN healthcare data

Methods: data imputation autoencoder data transformation deep learning multivariate imputation with chained equations

Theories: institutional theory social learning theory

A machine learning approach to identifying decision-making styles for managing customer relationships

2022 | Electronic Markets | Citations: 0

Authors: Tudoran, Ana Alina

Abstract: Decision-making styles have been studied in non-situational settings using the c ... Expand

Abstract: Decision-making styles have been studied in non-situational settings using the classical survey instrument. This study proposes a novel methodology for identifying decision-making styles in a real-world purchasing situation using only behavioral data and machine learning. We base our analysis on a two-week sample of 1,347,854 clickstream sessions from an e-commerce company and extract a series of parameters to infer the search goal, strategy, and decision difficulty. We implement a range of unsupervised algorithms, and we identify and validate three internally stable classes of decision-makers. One category corresponds to the classical style of satisficers; the other two subcategorize the maximisers' classical style. The customer’s entry channel preferences and movement patterns provide compelling support for the style's predictive validity. This study contributes to research and practice by proposing a new methodology to recognize the customer decision style in the e-commerce setting. Collapse

Topics: decision making information storage and retrieval system electronic commerce tablet mobile system

Methods: cluster analysis digital trace data computational algorithm machine learning unsupervised learning

Theories: theory of bounded rationality

Uncovering Thematic Dimensions of Fraudulent Emails using Topic Modelling

2021 | Americas Conference on Information Systems | Citations: 0

Authors: Bera, Debalina; Kim, Dan

Abstract: Recently, research on fraudulent email detection has expanded from its purely t ... Expand

Abstract: Recently, research on fraudulent email detection has expanded from its purely technological orientation into understanding the role of human behavior in security breaches. However, an area that has been lacking is literature/theory-grounded content analysis in social engineering threat. While there exists an extensive body of anecdotal subjective literature, factors that account for phishing attack's incessant success remain largely speculative. To better understand this, we propose a dimensional framework to identify the thematic nature of phishing emails that would not only help to understand the underlining technical subterfuge but also enhance our knowledge to effectively sense contextual social engineering tricks and the nature of such attacks, from multiple angles. To validate the dimensional elements, we will employ topic modeling techniques to analyze the themes of fraudulent email attack utilizing four benchmark datasets. Based on the analysis, this study discusses theoretical contributions and practical recommendations along with further directions of the research. Collapse

Topics: email phishing phishing email spam electronic mail IT security

Methods: topic model term frequency–inverse document frequency logistic regression k-means clustering self organizing map

An Architecture for Federated Learning Enabled Collaborative Intrusion Detection System

2021 | Americas Conference on Information Systems | Citations: 0

Authors: McOsker, Caitlin; Handlin, Michael; Li, Lei; Shahriar, Hossain; Zhao, Liang

Abstract: Intrusion Detection Systems (IDSs) are essential part of a security solution to ... Expand

Abstract: Intrusion Detection Systems (IDSs) are essential part of a security solution to monitor the network traffics and detect malicious attacks. In a collaborative IDS, multiple IDSs work together to effectively detect large scaled and across platforms or operating systems attacks. However, the participating nodes in a CIDS may hesitate to share their network traffic data due to privacy concerns. In this paper, we propose a federated learning enabled CIDS architecture leveraging its privacy-reserving feature. An unsupervised machine learning algorithm, Self-Organizing Maps (SOM), is adopted as the intrusion detection method. Based on our knowledge, this research is the first attempt incorporating SOM and federated learning into CIDS. We believe that the proposed framework can greatly improve both precision and recall of the intrusion detection. This paper is research in progress. We are in the process of developing a preliminary research prototype and designing experiments for validations. KeywordsCollaborative intrusion detection system, unsupervised learning, federated learning, anomaly detection. Collapse

Topics: privacy intrusion detection system zero-day vulnerability open source open source software project

Methods: machine learning federated learning experiment design artifact computational algorithm

Forecasting demand profiles of new products

2020 | Decision Support Systems | Citations: 3

Authors: van Steenbergen, R.M.; Mes, M.R.K.

Abstract: Nowadays, many companies face shorter product life cycles, increasing the need t ... Expand

Abstract: Nowadays, many companies face shorter product life cycles, increasing the need to properly forecast demand for newly introduced products. These forecasts allow them to support operational decisions, such as procurement and inventory control. However, forecasting the demand of new products is challenging compared to existing products, since historical sales data is not available as an indicator of future sales. Moreover, little attention has been paid in literature to quantitative methods for new product forecasting, especially with respect to quanti­ fying the uncertainty in demand. In this paper, we present a novel demand forecasting method denoted by DemandForest, which combines K-means, Random Forest, and Quantile Regression Forest. This machine learning-based approach combines the historical sales data of previously introduced products and product characteristics of existing and new products to make prelaunch forecasts and support inventory management decisions for new products. DemandForest clusters and predicts demand patterns, and predicts the quantiles of the total demand during an introduction period. We validate and illustrate our approach for forecasting and inventory management using real-world data sets of several companies. Compared to several benchmark methods, DemandForest provides the most accurate predictions, resulting in potential inventory savings of around 15% depending on lead times and service levels. Collapse

Topics: supply chain management database system inventory management system business-to-consumer business-to-business

Methods: computational algorithm random forest classification experiment cluster analysis k-means clustering

A scalable decision-tree-based method to explain interactions in dyadic data

2019 | Decision Support Systems | Citations: 0

Authors: Eiras-Franco, Carlos; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio

Abstract: Gaining relevant insight from a dyadic dataset, which describes interactions bet ... Expand

Abstract: Gaining relevant insight from a dyadic dataset, which describes interactions between two entities, is an open problem that has sparked the interest of researchers and industry data scientists alike. However, the existing methods have poor explainability, a quality that is becoming essential in certain applications. We describe an explainable and scalable method that, operating on dyadic datasets, obtains an easily interpretable high-level summary of the relationship between entities. To do this, we propose a quality measure, which can be configured to a level that suits the user, that factors in the explainability of the model. We report experiments that confirm better results for the proposed method over alternatives, in terms of both explainability and accuracy. We also analyse the method's capacity to extract relevant actionable information and to handle large datasets. Collapse

Topics: Apache Spark distributed computing recommender system website advertising management

Methods: cluster analysis computational algorithm experiment decision tree classification data transformation

Next-Purchase Prediction Using Projections of Discounted Purchasing Sequences

2018 | Business & Information Systems Engineering | Citations: 0

Authors: Shapoval, Katerina; Setzer, Thomas

Abstract: A primary task of customer relationship management (CRM) is the transformation ... Expand

Abstract: A primary task of customer relationship management (CRM) is the transformation of customer data into business value related to customer binding and development, for instance, by offering additional products that meet customers' needs. A customer's purchasing history (or sequence) is a promising feature to better anticipate customer needs, such as the next purchase intention. To operationalize this feature, sequences need to be aggregated before applying supervised prediction. That is because numerous sequences might exist with little support (number of observations) per unique sequence, discouraging inferences from past observations at the individual sequence level. In this paper the authors propose mechanisms to aggregate sequences to generalized purchasing types. The mechanisms group sequences according to their similarity but allow for giving higher weights to more recent purchases. The observed conversion rate per purchasing type can then be used to predict a customer's probability of a next purchase and target the customers most prone to purchasing a particular product. The biasvariance trade-off when applying the models to target customers with respect to the lift criterion are discussed. The mechanisms are tested on empirical data in the realm of cross-selling campaigns. Results show that the expected bias-variance behavior well predicts the lift achieved with the mechanisms. Results also show a superior performance of the proposed methods compared to commonly used segmentation-based approaches, different similarity measures, and popular class predictors. While the authors tested the approaches for CRM campaigns, their parameterization can be adjusted to operationalize sequential features of high cardinality also in other domains or business functions. Collapse

Topics: recommender system communication service infrastructure marketing management

Methods: cluster analysis logistic regression hidden Markov model self organizing map literature study

A Similarity-Based Approach for the All-Time Demand Prediction of New Automotive Spare Parts

2018 | HICSS | Citations: 0

Authors: Steuer, Daniel; Korevaar, Peter; Hutterer, Verena; Fromm, Hansjörg

Abstract: The all-time demand prediction of new automotive spare parts is an area that has ... Expand

Abstract: The all-time demand prediction of new automotive spare parts is an area that has not received much attention in theory and practice in the past. This paper presents a new approach for tackling this important, but complex problem. The approach assumes that newparts will have similar demand patterns as comparable parts had in the past. For the study, the demand histories of 80.718 spare parts were provided from a German automotive manufacturer. In a first step, historical fullcycle demand patterns were clustered based on similarity, and a representative demand pattern (RDP) was determined for each cluster. In a second step, a classification model was trained on the historical data relating demand patterns with selected parts characteristics. In a third step, the classifier was used to predict for each new part to which cluster it belongs. The RDP of this cluster is then used to calculate the part’s all-time demand. The developed models have been validated using standard quality measures used in analytics and will now be used in practice. Collapse

Topics: decision support

Methods: cluster analysis machine learning support vector machine literature study decision tree classification