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Journal articles on the topic 'Black-box Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

NAILA, FIRDAUS, and MANISHA DAWRA Ms. "Requirement Analysis of Black Box Testing." International Journal of Advances in Engineering & Scientific Research 1, no. 5 (2014): 19–23. https://doi.org/10.5281/zenodo.10721079.

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<strong><em>Abstract</em></strong> <em>                        </em><em>Black Box Testing is used when code of the module is not available. Black box testing treats the system as a “black-box”, so it doesn’t explicitly use knowledge of the internal structure or code. Or in other words the Test engineer need not know the internal working of the “Black box” or application. In such situations appropriate priorities can be given to different test cases, s

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Resende, David Nunes, Xiaowen Yu, Sílvia Gaia, and David V. Gibson. "Inside the Black Box." International Journal of Information Systems and Social Change 8, no. 4 (2017): 1–22. http://dx.doi.org/10.4018/ijissc.2017100101.

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This article uses an original three-phase approach for empirical assessment and comparative evaluation of the efficiency of university technology transfer. It is based on analysis of inputs and outputs of a disclosure phase followed by a value-add phase and a final license phase, using a multidimensional framework. The objective is to find university Technology Transfer Office (TTO) efficiency and effectiveness patterns for each phase as well as overall TT processes. A network Data Envelopment Analysis (DEA) model was used to analyze and describe the complicated TT operational processes using

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Leshkevich, Tatiana G. "Metaphors of the digital age and the Black Box Problem." Philosophy of Science and Technology 27, no. 1 (2022): 34–48. http://dx.doi.org/10.21146/2413-9084-2022-27-1-34-48.

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The article discusses the effects of the digital era, the driver of which is AI. The main goal is to focus on the Black Box Problem, “opacity of AI” and the possibility of Malicious Use of Artificial Intelligence. Three interconnected directions are interfaced. Firstly, in the context of the analysis of the digital age, the potential of metaphors is used, which makes it possible to describe digital transformations figuratively. Secondly, due to the growing demand for high technologies, the negative consequences of using AI are considered and a number of paradoxes of scientific and technolog

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Mickelborough, Jane. "Unpacking the black box." Reviews in Clinical Gerontology 7, no. 4 (1997): 287–88. http://dx.doi.org/10.1017/s0959259897007417.

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There are two black boxes. One is Pandora's, stuff of myth and legend, and remembered for releasing into the world all its cares and troubles, leaving only hope behind in the bottom of the box. The other, more recent and less mythical, is the in-flight recorder, used to monitor the events in an aircraft's flight, for later analysis in case of difficulty. Both these boxes have their analogy when we consider the role and current state of rehabilitation research in physiotherapy.

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Kim, Byol, and Rina Foygel Barber. "Black-box tests for algorithmic stability." Information and Inference: A Journal of the IMA 12, no. 4 (2023): 2690–719. http://dx.doi.org/10.1093/imaiai/iaad039.

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Abstract Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e.g. removal of a single data point) may affect the outputs of a regression algorithm. Knowing an algorithm’s stability properties is often useful for many downstream applications—for example, stability is known to lead to desirable generalization properties and predictive inference guarantees. However, many modern algorithms currently used in practice are too complex for a theoretical analysis of their stability properties, and thus we can only attempt to establish th

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Lamb, Anthony R. "Computer analysis: avoiding the ‘black box’ syndrome." Proceedings of the Institution of Civil Engineers - Civil Engineering 157, no. 3 (2004): 134–39. http://dx.doi.org/10.1680/cien.2004.157.3.134.

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Lamb, Anthony R. "Computer analysis: avoiding the ‘black box’ syndrome." Civil Engineering Innovation 1, no. 2 (2007): 93–99. http://dx.doi.org/10.1680/einn.2007.1.2.93.

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Lamb, Anthony R. "Computer analysis: avoiding the ‘black box’ syndrome." Civil Engineering Innovation 1, no. 2 (2007): 93–99. http://dx.doi.org/10.1680/jciei.2007.13258.

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Birkmeyer, John D., and Jean Y. Liu. "Decision analysis models: Opening the black box." Surgery 133, no. 1 (2003): 1–4. http://dx.doi.org/10.1067/msy.2003.21.

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Rossi, Dario, Elisa Sottile, and Paolo Veglia. "Black-box analysis of Internet P2P applications." Peer-to-Peer Networking and Applications 4, no. 2 (2010): 146–64. http://dx.doi.org/10.1007/s12083-010-0072-4.

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Hileman, L. C. "Penetrating the black box of phylogenetic analysis." Development 136, no. 24 (2009): 4067–68. http://dx.doi.org/10.1242/dev.038182.

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Nellaivadivelu, Guruswamy, Fabio Di Troia, and Mark Stamp. "Black box analysis of android malware detectors." Array 6 (July 2020): 100022. http://dx.doi.org/10.1016/j.array.2020.100022.

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13

Karadas, Georgiana, and Osman M. Karatepe. "Unraveling the black box." Employee Relations 41, no. 1 (2019): 67–83. http://dx.doi.org/10.1108/er-04-2017-0084.

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Purpose The purpose of this paper is to investigate the potential mediators that operate in the black box between high-performance work systems (HPWS) and employee outcomes. Design/methodology/approach The relationships the authors developed were assessed via data obtained from a time-lagged sample of customer-contact employees and their direct supervisors in the Romanian hotel industry. The study employed bias-corrected bootstrapping analysis to gauge the mediating effects. Findings The findings reveal that psychological capital mediates the impact of HPWS on work engagement. As hypothesized,

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Wu, Yaqiong, Chunhong Zhang, Wenlong Wu, Weilin Li, and Lianfei Lyu. "Genome-wide identification and analysis of the MADS-box gene family and its potential role in fruit ripening in black raspberry (Rubus occidentalis L.)." Journal of Berry Research 11, no. 2 (2021): 301–15. http://dx.doi.org/10.3233/jbr-200679.

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BACKGROUND: Black raspberry is a vital fruit crop with a high antioxidant function. MADS-box genes play an important role in the regulation of fruit development in angiosperms. OBJECTIVE: To understand the regulatory role of the MADS-box family, a total of 80 MADS-box genes were identified and analyzed. METHODS: The MADS-box genes in the black raspberry genome were analyzed using bioinformatics methods. Through an analysis of the promoter elements, the possible functions of different members of the family were predicted. The spatiotemporal expression patterns of members of the MADS-box family

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Akshaya, S., V. Panicker Allan, Johnson Joel, Asok Karthik, and Ravi Chinnu. "Black Box for Cars." Research and Applications of Web Development and Design 7, no. 2 (2024): 39–44. https://doi.org/10.5281/zenodo.12066354.

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<em>As vehicle accidents have being increased day by day it is high time to resolve this issue. To solve this issue and improve road safety, a "Black Box" system has been proposed. This system involves the installation of a device in vehicles that records various data, including engine temperature, speed, and location. Collected data, such as snapshots and sensor readings, are transmitted via GSM GPS module to authorities, enabling quick response and assistance in the event of accidents. This system aims to monitor accidents on roads and provide immediate help to casualties if needed. This sys

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Schiller, Devon. "The Black Box Grimaces Back." Grimace, Vol. 2, no. 1 (2017): 72–81. http://dx.doi.org/10.47659/m2.072..art.

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With the algorithmic age of computable emotions, an increasing number of digital artists base the form of their Internet or sculptural installation on Automated Facial Expression Analysis (AFEA), and its functionality achieved via the photographic documentation in face databases. These contemporary artists make visible a digital habit of thought that objectivates the human face into a plastic grotesque of grimacing extremis, and the self inside out into the universal or utilitarian. Yet, most AFEA systems – a term little clarified and much confused with facial recognition or biometrics – are “

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Schiller, Devon. "The Black Box Grimaces Back." Grimace, Vol. 2, no. 1 (2017): 72–81. http://dx.doi.org/10.47659/m2.072.art.

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With the algorithmic age of computable emotions, an increasing number of digital artists base the form of their Internet or sculptural installation on Automated Facial Expression Analysis (AFEA), and its functionality achieved via the photographic documentation in face databases. These contemporary artists make visible a digital habit of thought that objectivates the human face into a plastic grotesque of grimacing extremis, and the self inside out into the universal or utilitarian. Yet, most AFEA systems – a term little clarified and much confused with facial recognition or biometrics – are “

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Larson, Jeffrey, Matt Menickelly, and Stefan M. Wild. "Derivative-free optimization methods." Acta Numerica 28 (May 1, 2019): 287–404. http://dx.doi.org/10.1017/s0962492919000060.

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In many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of a black-box or simulation oracle that does not provide derivative information. Such settings necessitate the use of methods for derivative-free, or zeroth-order, optimization. We provide a review and perspectives on developments in these methods, with an emphasis on highlighting recent developments and on unifying treatment of such problems in the non-linear optimization and machine learning literature. We categorize

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Chen, Jie, JunWei Su, and JingYin Li. "Self-Coupling Black Box Model of a Dynamic System Based on ANN and Its Application." Mathematical Problems in Engineering 2020 (March 27, 2020): 1–12. http://dx.doi.org/10.1155/2020/5724831.

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The black box model of a dynamic system usually consists of just input and output. There is no correlation or coupling between the input and output. This paper proposes a self-coupling black box model method to realize the coupling between its input and output by introducing “virtual variables” to the black box model of a dynamic system considering the advantages of artificial neural network (ANN) in the system. The ANN is used for black box modeling. The modeling process of the self-coupling black box is illustrated through the simulation models and simulation analysis of the particle settlem

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Chitic, Raluca, Ali Osman Topal, and Franck Leprévost. "Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box." Applied Sciences 12, no. 14 (2022): 7339. http://dx.doi.org/10.3390/app12147339.

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Through the addition of humanly imperceptible noise to an image classified as belonging to a category ca, targeted adversarial attacks can lead convolutional neural networks (CNNs) to classify a modified image as belonging to any predefined target class ct≠ca. To achieve a better understanding of the inner workings of adversarial attacks, this study analyzes the adversarial images created by two completely opposite attacks against 10 ImageNet-trained CNNs. A total of 2×437 adversarial images are created by EAtarget,C, a black-box evolutionary algorithm (EA), and by the basic iterative method (

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Hartono, Firman Dwi, and Yuni Sugiarti. "PERBANDINGAN METODE EQUIVALENCE PARTITIONS DAN BOUNDARY VALUE ANALYSIS PADA PENGUJIAN BLACK BOX Literatur Review." Majalah Ilmiah METHODA 12, no. 2 (2022): 153–59. http://dx.doi.org/10.46880/methoda.vol12no2.pp153-159.

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This review literature reviews the Equivalence Partitioning and Boundary Value Analysis methods published between 2019 and 2020. Insights that arise from this review can guide researchers in their ongoing investigation of the implementation of Black Box, especially the Equivalence Partitioning and Boundary Value Analysis. From the review of 10 journals, this study found several substantive changes in the field of Black Box research. This includes an increase in the use of theory in research. This review literature aims to study software testing techniques in black box and improve better qualit

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Koga, Kazuki, and Kazuhiro Takemoto. "Simple Black-Box Universal Adversarial Attacks on Deep Neural Networks for Medical Image Classification." Algorithms 15, no. 5 (2022): 144. http://dx.doi.org/10.3390/a15050144.

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Universal adversarial attacks, which hinder most deep neural network (DNN) tasks using only a single perturbation called universal adversarial perturbation (UAP), are a realistic security threat to the practical application of a DNN for medical imaging. Given that computer-based systems are generally operated under a black-box condition in which only input queries are allowed and outputs are accessible, the impact of UAPs seems to be limited because well-used algorithms for generating UAPs are limited to white-box conditions in which adversaries can access model parameters. Nevertheless, we pr

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Juhász, László, and Irén Juhász Junger. "Spectral Analysis and Parameter Identification of Textile-Based Dye-Sensitized Solar Cells." Materials 11, no. 9 (2018): 1623. http://dx.doi.org/10.3390/ma11091623.

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Linearized equivalent electrical-circuit representation of dye-sensitized solar cells is helpful both for the better understanding of the physical processes in the cell as well as for various optimizations of the cells. White-box and grey-box modelling approaches are well-known and they are widely used for standard cell types. However, in the case of new cell types or the lack of deep knowledge of the cell’s physic such approaches may not be applicable immediately. In this article a black-box approach for such cases is presented applied together with spectral analysis. The spectral analysis an

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Billes, Marina, Anders Møller, and Michael Pradel. "Systematic black-box analysis of collaborative web applications." ACM SIGPLAN Notices 52, no. 6 (2017): 171–84. http://dx.doi.org/10.1145/3140587.3062364.

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Schroeder, Patrick J., and Bogdan Korel. "Black-box test reduction using input-output analysis." ACM SIGSOFT Software Engineering Notes 25, no. 5 (2000): 173–77. http://dx.doi.org/10.1145/347636.349042.

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Chen, Kai, DengGuo Feng, PuRui Su, and YingJun Zhang. "Black-box testing based on colorful taint analysis." Science China Information Sciences 55, no. 1 (2011): 171–83. http://dx.doi.org/10.1007/s11432-011-4291-y.

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Doerr, Benjamin, Carola Doerr, and Jing Yang. "Optimal parameter choices via precise black-box analysis." Theoretical Computer Science 801 (January 2020): 1–34. http://dx.doi.org/10.1016/j.tcs.2019.06.014.

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Banerjee, Ipsita, Siladitya Pal, and Spandan Maiti. "Computationally efficient black-box modeling for feasibility analysis." Computers & Chemical Engineering 34, no. 9 (2010): 1515–21. http://dx.doi.org/10.1016/j.compchemeng.2010.02.016.

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Vogt, Nina. "Unraveling the black box in neural signal analysis." Nature Methods 22, no. 5 (2025): 894. https://doi.org/10.1038/s41592-025-02715-1.

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Martynenko, S. I. "Robust Multigrid Technique for Black Box Software." Computational Methods in Applied Mathematics 6, no. 4 (2006): 413–35. http://dx.doi.org/10.2478/cmam-2006-0026.

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Abstract This paper presents a new robust multigrid technique for solving boundary value problems in a black box manner. To overcome the problem of robustness, the technique is based on the incorporation of adaptation of boundary value problems to numerical methods, control volume discretization and a new multigrid solver into a united computational algorithm. The special multiple coarse grid correction strategy makes it possible to obtain problem-independent transfer operators. As a result, most modes are approximated on coarse grids to make the task of the smoother on the finest grid the lea

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Pramudita, Rully. "Pengujian Black Box pada Aplikasi Ecampus Menggunakan Metode Equivalence Partitioning." INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics 4, no. 2 (2020): 193. http://dx.doi.org/10.51211/itbi.v4i2.1347.

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Salah satu tahapan penting dalam pengembangan sebuah perangkat lunak yaitu tahap pengujian. Teknik testing dibagi menjadi dua yaitu black box testing dan white box testing. Pada penelitian akan menerapkan teknik pengujian black box. Teknik pengujian black box terdiri dari, Equivalence Partitioning, Boundary Value Analysis / Limit Testing, Comparison Testing, Sample Testing, Robustness Testing, Behavior Testing, Requirement Testing, Performance Testing, Endurance Testing, Cause-Effect Relationship Testing. Salah satu yang akan diterapkan dalam penelitian ini yaitu teknik equivalence partitionin

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Pham, Cong, Trung-Nguyen Hoang, Cao-Truong Tran, and Viet-Binh Do. "GanTextKnockoff: stealing text sentiment analysis model functionality using synthetic data." Journal of Military Science and Technology, CSCE8 (December 30, 2024): 76–86. https://doi.org/10.54939/1859-1043.j.mst.csce8.2024.76-86.

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Today, black-box machine learning models are often subject to extraction attacks that aim to retrieve their internal information. Black-box model extraction attacks are typically conducted by providing input data and, based on observing the output results, constructing a new model that functions equivalently to the original. This process is usually carried out by leveraging available data from public repositories or synthetic data generated by generative models. Most model extraction attack methods using synthetic data have been concentrated in the field of computer vision, with minimal resear

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Pham, Cong, Viet-Binh Do, Trung Nguyen Hoang, and Cao Truong Tran. "Black-box model functionality stealing for Vietnamese sentiment analysis." Journal of Military Science and Technology 104 (June 25, 2025): 144–54. https://doi.org/10.54939/1859-1043.j.mst.104.2025.144-154.

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Black-box deep learning models often keep critical components such as model architecture, hyperparameters, and training data confidential, allowing users to observe only the inputs and outputs without understanding their internal workings. Consequently, there is growing interested in developing "knockoff" models that replicate the behavior of these black-box models without direct access to internal details. We have conducted extensive studies on function extraction attacks targeting English text sentiment analysis models. By employing random or adaptive sampling methods, we have successfully r

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Pintelas, Emmanuel, Ioannis E. Livieris, and Panagiotis Pintelas. "A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability." Algorithms 13, no. 1 (2020): 17. http://dx.doi.org/10.3390/a13010017.

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Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decisions on a wide range of applications. Nevertheless, we still seek to understand and explain how these models work and make decisions. Explainability and interpretability in machine learning is a significant issue, since in most of real-world problems it is considered essential to understand and explain the model’s prediction mechanism in order

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Petrick, Elizabeth R. "Building the Black Box: Cyberneticians and Complex Systems." Science, Technology, & Human Values 45, no. 4 (2019): 575–95. http://dx.doi.org/10.1177/0162243919881212.

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In the 1950s and 1960s, cyberneticians defined and utilized a concept previously described by electronic engineers: the black box. They were interested in how it might aid them, as both a metaphor and as a physical or mathematical model, in their analysis of complex human-machine systems. The black box evolved as they applied it in new ways, across a range of scientific fields, from an unnamed concept involving inputs and outputs, to digital representations of the human brain, to white boxes that might be used to replicate black boxes. The diversity of understandings of the black box reflected

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Pasaribu, Johni Setiady. "Perbandingan Pengujian Boundary Value Analysis, Equivalence Partitioning dan Error Guessy (Studi Kasus Indeks Nilai)." Jurnal ICT : Information Communication & Technology 20, no. 2 (2021): 210–17. http://dx.doi.org/10.36054/jict-ikmi.v20i2.388.

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Software testing is a critical and time-consuming element of the software development life cycle (SDLC). Software testing determines the quality of a software before it is used by end users. The main goal is to find software errors or defects so that these errors or defects can be corrected in this testing phase. There are two general techniques for detecting software errors, namely: Black Box and White Box Testing. Black Box testing technique is used for validation, namely whether you have built the right software and White Box testing is used for verification, namely whether the software mad

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Dobslaw, Felix, Robert Feldt, and Francisco Gomes de Oliveira Neto. "Automated black-box boundary value detection." PeerJ Computer Science 9 (November 7, 2023): e1625. http://dx.doi.org/10.7717/peerj-cs.1625.

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Software systems typically have an input domain that can be subdivided into sub-domains, each of which generates similar or related outputs. Testing it on the boundaries between these sub-domains is critical to ensure high-quality software. Therefore, boundary value analysis and testing have been a fundamental part of the software testing toolbox for a long time and are typically taught early to software engineering students. Despite its many argued benefits, boundary value analysis for a given software specification or application is typically described in abstract terms. This allows for vari

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Mansour, Nizar, Emna Gara, and Chiha Gaha. "Getting inside the black box." Personnel Review 43, no. 4 (2014): 490–514. http://dx.doi.org/10.1108/pr-03-2013-0052.

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Purpose – The purpose of this paper is to explore, and eventually unlocking, the “black box” problem by addressing the potential mediating role of human capital and organizational commitment in the relationship between high performance work systems (HPWS) and perceived firm performance in the Tunisian financial industry. Design/methodology/approach – Based on the strategic human resource management (SHRM) theory, the authors developed a model that links HPWS to perceived organizational performance through human capital and employee organizational commitment. Data collected from 351 respondents

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Gerber, B. S., T. G. Tape, R. S. Wigton, and P. S. Heckerling. "Entering the Black Box of Neural Networks." Methods of Information in Medicine 42, no. 03 (2003): 287–96. http://dx.doi.org/10.1055/s-0038-1634363.

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Summary Objectives: Artificial neural networks have proved to be accurate predictive instruments in several medical domains, but have been criticized for failing to specify the information upon which their predictions are based. We used methods of relevance analysis and sensitivity analysis to determine the most important predictor variables for a validated neural network for community-acquired pneumonia. Methods: We studied a feed-forward, back-propagation neural network trained to predict pneumonia among patients presenting to an emergency department with fever or respiratory complaints. We

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Hu, Ran, Nan Ma, Bing Li, et al. "Black-Box Modelling of Active Distribution Network Devices Based on Neural ODEs." Journal of Physics: Conference Series 2826, no. 1 (2024): 012029. http://dx.doi.org/10.1088/1742-6596/2826/1/012029.

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Abstract The morphological structure and dynamic characteristics of power distribution systems are rapidly evolving due to the widespread application of distributed renewable energy and the rapid advancement of power electronics technology. The stability analysis of these complex new distribution systems depends on electromagnetic transient (EMT) simulation. However, the intellectual property rights protection by manufacturers leads to many distribution network devices that can only use black-box models with missing parameters, which challenges the simulation. This reduces the accuracy of the

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Mani, Vaidyanath, and Robert E. Hartwig. "Duality and Black Box Interpolation I: The One Variable Nonderogatory Case." SIAM Journal on Matrix Analysis and Applications 16, no. 1 (1995): 21–28. http://dx.doi.org/10.1137/s0895479892234922.

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Koshy, Roshan, and Vineetha V. Nair. "Black Box for Accident Analysis Using MATLAB-Image Processing." International Journal of Computer Applications Technology and Research 6, no. 5 (2017): 229–33. http://dx.doi.org/10.7753/ijcatr0605.1003.

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Siordia, O. S., I. M. de Diego, C. Conde, and E. Cabello. "Wireless In-Vehicle Compliant Black Box: For Accident Analysis." IEEE Vehicular Technology Magazine 7, no. 3 (2012): 80–89. http://dx.doi.org/10.1109/mvt.2012.2203692.

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Takefuji, Yoshiyasu. "Black box analysis with linear regression on global warming." Hygiene and Environmental Health Advances 12 (December 2024): 100109. http://dx.doi.org/10.1016/j.heha.2024.100109.

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Carabantes, Manuel. "Black-box artificial intelligence: an epistemological and critical analysis." AI & SOCIETY 35, no. 2 (2019): 309–17. http://dx.doi.org/10.1007/s00146-019-00888-w.

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Lim, Eldin Wee Chuan, and Jin Rou New. "Black-Box Optimization by Fourier Analysis and Swarm Intelligence." JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 45, no. 6 (2012): 417–28. http://dx.doi.org/10.1252/jcej.12we002.

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Yang, Homer. "Intraoperative automated ST segment analysis: a reliable ‘Black Box’?" Canadian Journal of Anaesthesia 43, no. 10 (1996): 1041–51. http://dx.doi.org/10.1007/bf03011907.

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Filiol, Eric. "Malware Pattern Scanning Schemes Secure Against Black-box Analysis." Journal in Computer Virology 2, no. 1 (2006): 35–50. http://dx.doi.org/10.1007/s11416-006-0009-x.

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Priyadarshini, MS V. Nisha, D. R. Lakshmi D R Lakshmi, Sirani Sunitha, et al. "Automobile Black Box System for Accident Analysis using IOT." International Journal of Advances in Engineering and Management 7, no. 3 (2025): 572–79. https://doi.org/10.35629/5252-0703572579.

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The Automobile Black Box System for Accident Analysis using Io Tis amodern solution aimed atim proving road safety through real-time monitoring and data collection.Inspired by the flight data recorders in aviation,this systemis designed to monitorand analyzecrucialvehicleparameters to detect,record, and report accidents or mishaps. The system integrates multiple sensors such as alcohol sensors, CO2 sensors, temperature sen- sors, ultra sonic sensors, collision sensors, anddoorsensors, which continuously track the vehicle’s environment and the driver’s behavior. Data from these sensors is proce

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Zhang, Chao, and Yu Wang. "Research on the Structure of Authentication Protocol Analysis Based on MSCs/Promela." Advanced Materials Research 989-994 (July 2014): 4698–703. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4698.

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Abstract:

To discover the existing or possibly existing vulnerability of present authentication protocol, we proposed a structure of authentication protocol analysis, which consist of white box analysis, black box analysis and indicator system as the three main functional components. White box analysis makes use of the transformation from MSCs (Message Sequence Charts) to Promela (Process Meta Language), the input language of the remarkable model checker SPIN; black box analysis is based on the attack platform of authentication protocol analysis; indicator system is decided by deducibility restraint met

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