The Core Industry Manufacturing Process of Electronics Assembly Based on Smart Manufacturing

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

Authors: Chen, Rongli; Chen, Xiaozhong; Wang, Lei; Li, Jianxin

Abstract: This research takes a case study approach to show the development of a diverse a ... Expand

Abstract: This research takes a case study approach to show the development of a diverse adoption and product strategy distinct from the core manufacturing industry process. It explains the development status in all aspects of smart manufacturing, via the example of ceramic circuit board manufacturing and electronic assembly, and outlines future smart manufacturing plans and processes. The research proposed two experiments using artificial intelligence and deep learning to demonstrate the problems and solutions regarding methods in manufacturing and factory facilities, respectively. In the first experiment, a Bayesian network inference is used to find the cause of the problem of metal residues between electronic circuits through key process and quality correlations. In the second experiment, a convolutional neural network is used to identify false defects that were overinspected during automatic optical inspection. This improves the manufacturing process by enhancing the yield rate and reducing cost. The contributions of the study built in circuit board production. Smart manufacturing, with the application of a Bayesian network to an Internet of Things setup, has addressed the problem of residue and redundant conductors on the edge of the ceramic circuit board pattern, and has improved and prevented leakage and high-frequency interference. The convolutional neural network and deep learning were used to improve the accuracy of the automatic optical inspection system, reduce the current manual review ratio, save labor costs, and provide defect classification as a reference for preprocess improvement. Collapse

Topics: artificial intelligence smart manufacturing production management Python internet of things

Methods: experiment deep learning case study convolutional neural network Bayesian network

Prediction of hotel booking cancellations: Integration of machine learning and probability model based on interpretable feature interaction

2023 | Decision Support Systems | Citations: 0

Authors: Chen, Shuixia; Ngai, Eric W.T.; Ku, Yaoyao; Xu, Zeshui; Gou, Xunjie; Zhang, Chenxi

Abstract: Reliable hotel cancellation prediction can help establish appropriate operationa ... Expand

Abstract: Reliable hotel cancellation prediction can help establish appropriate operational strategies for hotel manage­ ment. In this sector, personal name records (PNR) data may be the most representative information source for prediction tasks. Despite the popularity of PNR, its inherent lack of availability has been commonly disregarded in the literature. Existing studies have directly input PNR into high-dimensional machine learning (ML) models to achieve cancellation predictions. Another type of model generates cancellation prediction based on the proba­ bility modeling of samples. In this study, we propose an interpretable feature interaction method to enrich the existing PNR information. Thereafter, we empirically assess the prediction performance of the two model classes. This study specifically determines whether or not the two methods can cross-fertilize each other to improve cancellation prediction. To do so, we propose a model integrating Bayesian networks (BNs) and Lasso regression for this prediction task. This study utilizes BNs for the probability model consistent with our correlated variables and dichotomous prediction setting. Moreover, we use a linear ML model (i.e., Lasso regression), given its ad­ vantages in reducing ineffective predictors and transparency for ranking feature importance. Empirical results show that the proposed integration model has better prediction performance, and the obtained BN estimators and interactive features are the most important predictors. This study contributes to the booking cancellation liter­ ature by proposing an interpretable feature interaction and a prediction method integrating two types of effective models. The obtained accurate and interpretable cancellation prediction further contributes to offering practical implications to hoteliers in managerial decision-making. Collapse

Topics: missing data decision making competitive advantage price management marketing management

Methods: lasso regression experiment computational algorithm robustness check parametric test

Guidance in Business Intelligence & Analytics Systems: A Review and Research Agenda

2023 | European Conference On Information Systems | Citations: 0

Authors: Gunklach, Jonas; Nadj, Mario

Abstract: While the data amount grows exponentially, the number of people with analytical ... Expand

Abstract: While the data amount grows exponentially, the number of people with analytical and technical skills is only slowly increasing. This skill gap is putting pressure on the labor market and increasing the need for personnel with these skills. At the same time, companies are forced to think of alternative ways to empower their less-skilled workforce to take on Business Intelligence and Analytics (BI&A) tasks. One promising attempt to address these challenges may turn to the concept of guidance. However, the current body of research on guidance in BI&A systems is scattered and lacks a structured investigation from which future research avenues can be derived. To address this gap, this article analyzes five categories, namely BI&A phases, guidance degree, guidance generation, user roles, and interactivity form. Reviewing 82 articles, our contribution is to synopsize articles on guidance in BI&A systems and to suggest five research avenues. Collapse

Topics: analytical information system data visualization data quality business intelligence artificial intelligence

Methods: exploratory data analysis machine learning literature sample time series analysis longitudinal research

AI Labor Markets: Toward a Dynamic Skills-Based Approach to Measurement

2023 | International Conference on Information Systems | Citations: 0

Authors: Kim, Jeongmin; Rai, Arun; Lin, Yu-Kai

Abstract: Artificial intelligence (AI) is transforming the nature of work and reshaping l ... Expand

Abstract: Artificial intelligence (AI) is transforming the nature of work and reshaping labor markets. Viewing labor as a bundle of skills, recent research has analyzed AI skills and offered important insights about the impacts of AI on labor markets. We add to this ongoing discourse and argue that taking a dynamic skill-based approach to measurement is critical: just like the development of AI is emergent and ever-evolving, so are AI skills. Taking stock of the literature, we show that existing studies tend to take a static approach to measuring AI skills, which fails to fully reflect the dynamic phenomenon of AI skills and could cause measurement errors. We propose a dynamic co-occurrence method and demonstrate that it performs better than the extant static methods, which can cause severe Type I and II errors, omit emerging AI skills, and temporally over-and underestimate the demands for AI skills and jobs. Collapse

Topics: artificial intelligence online labor market artificial general intelligence expert system

Methods: literature study machine learning robustness check artificial neural network natural language processing

Bayesian Social Subgraph Generative Models: Social Network Twins using Belief Networks and Ego Behavior Models

2023 | HICSS | Citations: 0

Authors: Naidu, N.; El-Gayar, O.

Abstract: A key assumption of a Subgraph Generative Model (SUGM) for sparse networks is t ... Expand

Abstract: A key assumption of a Subgraph Generative Model (SUGM) for sparse networks is that a subgraph is independent of lower order subgraphs in a sparse network. This is not entirely true especially for nonsparse networks. Additionally, the generated networks lack the typical properties of a social network because of an assumption of random growth for nodes and edges. Finally, there is no concept for explicit ego choice or bias when connecting to dyadic or triadic relationships. We develop a novel graph generative model referred to as the Bayesian Social Subgraph Generative Model (BASSUGM). We ground the BASSUGM in a proposed sociological model and leverage Bayesian tools like belief networks. We introduce novel concepts like the networks' macro theme when combines with an ego's individuality realizes the ego's intent. We also demonstrate how the social network twin generated with BASSUGM outperforms SUGM for non-sparse, small, social, networks. Collapse

Topics: social network logistics management telephone

Methods: cluster analysis Bayesian network experiment hierarchical linear modeling computational algorithm

Theories: probability theory graph theory

Bayesian Networks for Interpretable Cyberattack Detection

2023 | HICSS | Citations: 0

Authors: Yang, B.; Hoffman, M.; Brown, N.

Abstract: The challenge of cyberattack detection can be illustrated by the complexity of ... Expand

Abstract: The challenge of cyberattack detection can be illustrated by the complexity of the MITRE ATT&CK TM matrix, which catalogues >200 attack techniques (most with multiple sub-techniques). To reliably detect cyberattacks, we propose an evidence-based approach which fuses multiple cyber events over varying time periods to help differentiate normal from malicious behavior. We use Bayesian Networks (BNs)probabilistic graphical models consisting of a set of variables and their conditional dependencies -for fusion/classification due to their interpretable nature, ability to tolerate sparse or imbalanced data, and resistance to overfitting. Our technique utilizes a small collection of expert-informed cyber intrusion indicators to create a hybrid detection system that combines data-driven training with expert knowledge to form a host-based intrusion detection system (HIDS). We demonstrate a software pipeline for efficiently generating and evaluating various BN classifier architectures for specific datasets and discuss explainability benefits thereof. Collapse

Topics: shapley additive explanation application programming interface Python Microsoft Windows intrusion detection system

Methods: random forest classification naïve bayes machine learning cluster analysis experiment

Unbox the Black-Box: Predict and Interpret YouTube Viewership Using Deep Learning

2023 | Journal of Management Information Systems | Citations: 0

Authors: Xie, Jiaheng; Chai, Yidong; Liu, Xiao

Abstract: As video-sharing sites emerge as a critical part of the social media landscape, ... Expand

Abstract: As video-sharing sites emerge as a critical part of the social media landscape, video viewership prediction becomes essential for content creators and businesses to optimize influence and marketing outreach with minimum budgets. Although deep learning champions viewership prediction, it lacks interpretability, which is required by regulators and is fundamental to the prioritization of the video production process and promoting trust in algorithms. Existing interpretable predictive models face the challenges of imprecise interpretation and negligence of unstructured data. Following the design-science paradigm, we propose a novel Precise Wide-and-Deep Learning (PrecWD) to accurately predict viewership with unstructured video data and well-established features while precisely interpreting feature effects. PrecWD's prediction outperforms benchmarks in two case studies and achieves superior interpretability in two user studies. We contribute to IS knowledge base by enabling precise interpretability in video-based predictive analytics and contribute nascent design theory with generalizable model design principles. Our system is deployable to improve video-based social media presence. Collapse

Topics: YouTube web page explainable artificial intelligence shapley additive explanation analytical information system

Methods: deep learning case study machine learning design principle computational algorithm

A Combined Weighting Based Large Scale Group Decision Making Framework for MOOC Group Recommendation

2023 | Group Decision and Negotiation | Citations: 0

Authors: Zhang, Chonghui; Su, Weihua; Chen, Sichao; Zeng, Shouzhen; Liao, Huchang

Abstract: Massive open online courses (MOOC) are free learning courses based on online pla ... Expand

Abstract: Massive open online courses (MOOC) are free learning courses based on online platforms for higher education, which not only promote the open sharing of learning resources, but also lead to serious information overload. However, there are many courses on MOOCs, and it can be difficult for users to choose courses that match their individual or group preferences. Therefore, a combined weighting based large-scale group decision-making approach is proposed to implement MOOC group recommendations. First, based on the MOOC operation mode, we decompose the course content into three stages, namely pre-class, in-class, and post-class, and then the curriculum-arrangement-movement- performance evaluation framework is constructed. Second, the probabilistic linguistic criteria importance through intercriteria correlation method is employed to obtain the objective weighting of the criterion. Meanwhile, the word embedding model is utilized to vectorize online reviews, and the subjective weighting of the criteria are acquired by calculating the text similarity. The combined weighting then can be obtained by fusing the subjective and objective weighting. Based on this, the PL-MULTIMIIRA approach and Borda rule is employed to rank the alternatives for group recommendation, and an easy-to-use formula for group satisfaction is proposed to evaluate the effect of the proposed method. Furthermore, a case study is conducted to group recommendations for statistical MOOCs. Finally, the robustness and effectiveness of the proposed approach were verified through sensitivity analysis as well as comparative analysis. Collapse

Topics: online review online learning system user-generated content decision making recommender system

Methods: descriptive statistic GloVe cluster analysis trade-off analysis method computational algorithm

Predicting Analysts' Needs for Explainable Artificial Intelligence (XAI) in Cybersecurity Analysis

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Zhong, Chen; Ni, Qinwei; Chen, Ping

Abstract: As cyberattacks become more sophisticated and prevalent, analysts face several ... Expand

Abstract: As cyberattacks become more sophisticated and prevalent, analysts face several new challenges, such as a data influx and a high percentage of false alerts. Many AI-driven tools have been utilized in detection systems to help analysts find anomalies. However, the black-box nature of many AI models makes it difficult for analysts to utilize them effectively. This leads to a growing need for Explainable AI (XAI) to make AI models more transparent. However, there is a lack of personalized XAI that would enable analysts to receive explanations tailored to their needs. To address this problem, we develop a system that can predict an analyst's need for explainability based on a Bayesian Network (BN). We first identify the factors that impact analysts' needs for explainability and then use a data-driven method to build the network structure. The performance of the system will be evaluated in an experiment involving twenty participants. Collapse

Topics: artificial intelligence explainable artificial intelligence

Methods: Bayesian network sensitivity analysis experiment survey artificial neural network

Exploring investors' expectancies and its impact on project funding success likelihood in crowdfunding by using text analytics and Bayesian networks

2022 | Decision Support Systems | Citations: 6

Authors: Costello, Francis Joseph; Lee, Kun Chang

Abstract: Crowdfunding has become immensely popular today, allowing entrepreneurs to prese ... Expand

Abstract: Crowdfunding has become immensely popular today, allowing entrepreneurs to present innovative ideas to a broad audience of potential investors. However, due to the online nature of crowdfunding, investors have to use clues only available on the website to decide on whether to invest or not. Grounded in language expectancy theory (LET), we proposed and tested hypotheses suggesting that, when no knowledge of the entrepreneur is available, investors have to use language expectancy as a way to inform them on their investment decisions. Furthermore, we propose that communication content in entrepreneurial narratives such as vague communi­ cation and linguistics affect the quality of information leading to a violation of language expectancy. We also postulate that this will manifest in affect intensity and in two-sided persuasion. We separated the description and risks and challenges (R&C) sections from Kickstarter and performed discrete analyses with regressions to further test two-sidedness. Next, we sought to understand causal knowledge of the underlying target class by imple­ menting a Bayesian Network to find the conditional probability of the variables before attempting to find a nearoptimal probability of funding success using a genetic algorithm. We found robust support for our hypotheses and helped shed light on which information is received and interpreted by investors leading to a greater like­ lihood of funding success. Overall, this approach sheds new light on the role of language within crowdfunding literature. Collapse

Topics: crowdsourcing crowdfunding decision support data quality

Methods: Bayesian network computational algorithm regression analysis method statistical hypothesis test descriptive statistic

Theories: expectancy theory of motivation lemon market theory