Data has become an increasingly important component in contemporary business operations, epitomised by the rise of the Business Intelligence system, data analytics, and data visualisations. It has ...been associated with increased productivity and the development of new business opportunities. But the use of data is sometimes also associated with poor decision-making, either because of the quality of the data on which decisions are made, or because of the ways in which that data is used. This paper explores the problem of dangerous data in commercial contexts: those situations where the use of data contributes to worse outcomes.t
Criminalizing China Lewis, Margaret K
The journal of criminal law & criminology,
12/2020, Letnik:
111, Številka:
1
Journal Article
Recenzirano
"The Department of Justice launched the China Initiative in November 2018 to counter national security threats emanating from the People's Republic of China (PRC). By June 2020, the Federal Bureau of ...Investigation had approximately two thousand active investigations under the Initiative.
The specifics of business intelligence systems compared to operational IS motivate the necessity to research the business intelligence systems acceptance determinants separately. The authors followed ...an exploratory approach in order to conceptualize a business intelligence acceptance model. Their findings show that in the Business Intelligence Systems context, there is a significant emphasis on organizational factors, such as result demonstrability, social influence, and facilitating conditions with sufficient resources that help build an adequate information culture all substantially influencing the effective acceptance of business intelligence systems.
This case is designed to illustrate how to utilize a business intelligence framework and a geographic information system (GIS) to make better decisions in the banking industry. The case started when ...Jong, the senior certified analytic professional, and Kampu, the GIS solutions expert were drafting a proposal presentation to help ABC Bank of Thailand improve its home loan appraisal process. The current appraisal process was time-consuming and the appraisal value from the internal and external appraisers were very different as the appraisal process relied heavily on the opinions and judgements from all appraisal staffs. Currently, there was a home loan application pending for Hugo, the appraisal manager of ABC Bank of Thailand, to make the decision on the final appraised value. This was a great opportunity for Jong and Kampu to revisit the current appraisal process and to demonstrate how business intelligence and GIS could aid in the appraisal decision. Additionally, the proposed GIS-based BI dashboard allowed the appraisal team to the analyze data-related to the appraised property in real-time. Eventually, Jong and Kampu had to make a decision on what appraised value Hugo should recommend to the home loan committee members.
This study investigated the role and influence of adaptive e-learning co-design on marketing performance of higher education institutions and provides recommendations for the academic community for ...improving learning and marketing performance. This paper employed a mixed method approach to better understand the role of adaptive e-learning co-design. The qualitative approach reviewed the relevant literature and used unstructured interviews. The quantitative method applied a questionnaire based on the instruction of each variable. It was shared with 257 participants, and then the data were processed with the structural equation modelling technique. In collecting information, a sharing session class was created with lecturers and students at universities in Toba, Indonesia. It was found that adaptive e-learning co-design mediates the influence of quality information on successful business intelligence. Adaptive e-learning co-design increases continuous innovation, whilst successful business intelligence improves higher education marketing performance. Adaptive e-learning co-design invites students and lecturers to be part of the design team of co-production knowledge and experience. Additionally, it demands higher education be a co-creator of values and implies a strategic orientation towards collaboration between stakeholders to integrate e-learning processes. Adaptive e-learning describes the role of stakeholders in the design and bidding creation process. This study assessed active students in two consecutive semesters participating in e-learning. adaptive e-learning co-design can improve higher education marketing performance through successful business intelligence.
Over time, Decision Support Systems have helped decision makers solve complex problems through Operational Research and Simulation. Nowadays, data explosion is having a profound effect on the ways in ...which many sectors operate. This advent of massive data gives rise to new concepts and requires new methods and analysis tools. In this paper, we highlight the role of simulation in Business Analytics. In a framework-based analytics, simulation is a technique that can be incorporated into predictive or prescriptive stage. For that, we have posed research questions to limit results to what give a comprehensive description of models, techniques and architectures used in the hybridization between simulation and business analytics. The presented analyses confirm that simulation remains an indispensable mechanism for adding value to analytics project and the coupling between the two techniques is in its embryonic phase. A conclusion presented prospects and future improvements found during the writing of the research.
This article was focused on establishing whether Business Intelligence (BI) systems provide sustainability to commercial banks by influencing their financial condition. As part of the search for a ...solution to the research problem, a hypothesis was formulated which assumes that the use of the Business Intelligence management system improves the financial condition of commercial banks. To assess this impact, a novel comparative method was used, which assumed comparing financial condition indicators in three aspects: before and after the implementation of the Business Intelligence system (comparison over time), with average indicators of a group of banks (comparison to the industry), with reference to changes in the overall economic situation. As a result of the method used, a synthetic indicator of the impact of using Business Intelligence (ABI) was calculated. This study was conducted in relation to six out of the thirteen largest commercial banks listed on the Warsaw Stock Exchange in 2020, which have implemented the Business Intelligence system since 2001. The assets of the examined banks cover 60% of the assets of commercial banks in Poland. As a result of the study, a positive impact of using the BI system on selected areas of the financial condition of commercial banks was identified. In particular, this impact relates to areas of productivity, the quality of assets and liabilities, profitability and debt. The generalized results of this study allow for the determination of cause and effect relationships between the use of the BI system in commercial banks and the improvement of the financial condition indicators as well as sustainability banking.
Purpose
The purpose of this paper is to conduct a comprehensive review of the noteworthy contributions made in the area of the Feedforward neural network (FNN) to improve its generalization ...performance and convergence rate (learning speed); to identify new research directions that will help researchers to design new, simple and efficient algorithms and users to implement optimal designed FNNs for solving complex problems; and to explore the wide applications of the reviewed FNN algorithms in solving real-world management, engineering and health sciences problems and demonstrate the advantages of these algorithms in enhancing decision making for practical operations.
Design/methodology/approach
The FNN has gained much popularity during the last three decades. Therefore, the authors have focused on algorithms proposed during the last three decades. The selected databases were searched with popular keywords: “generalization performance,” “learning rate,” “overfitting” and “fixed and cascade architecture.” Combinations of the keywords were also used to get more relevant results. Duplicated articles in the databases, non-English language, and matched keywords but out of scope, were discarded.
Findings
The authors studied a total of 80 articles and classified them into six categories according to the nature of the algorithms proposed in these articles which aimed at improving the generalization performance and convergence rate of FNNs. To review and discuss all the six categories would result in the paper being too long. Therefore, the authors further divided the six categories into two parts (i.e. Part I and Part II). The current paper, Part I, investigates two categories that focus on learning algorithms (i.e. gradient learning algorithms for network training and gradient-free learning algorithms). Furthermore, the remaining four categories which mainly explore optimization techniques are reviewed in Part II (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks and metaheuristic search algorithms). For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part II): Neural networks optimization techniques and applications” is referred to as Part II. This results in a division of 80 articles into 38 and 42 for Part I and Part II, respectively. After discussing the FNN algorithms with their technical merits and limitations, along with real-world management, engineering and health sciences applications for each individual category, the authors suggest seven (three in Part I and other four in Part II) new future directions which can contribute to strengthening the literature.
Research limitations/implications
The FNN contributions are numerous and cannot be covered in a single study. The authors remain focused on learning algorithms and optimization techniques, along with their application to real-world problems, proposing to improve the generalization performance and convergence rate of FNNs with the characteristics of computing optimal hyperparameters, connection weights, hidden units, selecting an appropriate network architecture rather than trial and error approaches and avoiding overfitting.
Practical implications
This study will help researchers and practitioners to deeply understand the existing algorithms merits of FNNs with limitations, research gaps, application areas and changes in research studies in the last three decades. Moreover, the user, after having in-depth knowledge by understanding the applications of algorithms in the real world, may apply appropriate FNN algorithms to get optimal results in the shortest possible time, with less effort, for their specific application area problems.
Originality/value
The existing literature surveys are limited in scope due to comparative study of the algorithms, studying algorithms application areas and focusing on specific techniques. This implies that the existing surveys are focused on studying some specific algorithms or their applications (e.g. pruning algorithms, constructive algorithms, etc.). In this work, the authors propose a comprehensive review of different categories, along with their real-world applications, that may affect FNN generalization performance and convergence rate. This makes the classification scheme novel and significant.
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