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41.
Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related
death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural
and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses
(CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue.
The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a
key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach
such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations
that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented
operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations
as efficient and powerful tools for biomedical image processing. 相似文献
42.
Hendrick HW 《Applied ergonomics》2008,39(4):418-426
Based on evidence accumulated during the author's 45 years of professional experience, the author presents 23 important “lessons learned” regarding applying ergonomics to systems. Documented results from reported cases or other evidence are presented to validate each of these practical learning points. 相似文献
43.
Vimal Singh 《Automatica》2008,44(1):282-285
A novel criterion for the global asymptotic stability of a class of digital filters utilizing single saturation nonlinearity is presented. An example showing the effectiveness of the present criterion is given. 相似文献
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《The Journal of Strategic Information Systems》2022,31(4):101746
The Journal of Strategic Information Systems (JSIS) was first published December 1991 by Robert (Bob) D. Galliers, its Founding Editor-in-Chief (EiC). Early on Bob invited me to join the JSIS International Editorial Board1 (the Board then being more operational, with members serving as quasi-AEs, an arrangement we’ve reinstituted more recently). I became Pacific Asia Region Editor in 2003, with JSIS shifting from region editors to Senior Editors in 2007, in which role I served the journal through the end of 2018. Mid-2018 Bob and Sirkka (Sirkka Jarvenpaa joined as co-EiC in 2000) jointly decided it was time; and announced they would both step down from their co-EiC roles at the end of that year. In September 2018, I was endorsed by Bob and Sirkka, the Senior Editors and Elsevier, to succeed Bob and Sirkka and assume the role of Editor-in-Chief of JSIS commencing 1 January 2019. I served as Editor-in-Chief through 1 July 2021 after which Yolande Chan and I served as co-Editors-in-Chief. Now the end of 2022, I am stepping down from operational involvement with the journal. In this my final editorial I focus on my time as EiC, also reflecting on my almost 3 decades with the journal. I much appreciate being invited to write these brief ruminations and am honoured to be included amongst the journal’s Emeritus, along with the esteemed Robert Galliers and Sirkka Jarvenpaa. 相似文献
48.
This paper is concerned with the adaptive stabilization problem of uncertain input delayed systems.A solution to this problem is given for a class of uncertain nonlinear systems with time-varying delays in both state and input.An adaptive asymptotically stabilizing controller,which can guarantee the stability of the closed-loop system and the convergence of the original system state,is designed by means of the Lyapunov-Krasovskii functional stability theory combined with linear matrix inequalities (LMIs) an... 相似文献
49.
The stability of a class of Markov Jump Linear Systems characterized by piecewise-constant transition rates and system dynamics is investigated. For these Switching Markov Jump Linear Systems, mean square stability is analyzed through the time evolution of the second-order moment of the state. The main result is a sufficient condition that guarantees mean square stability under constraints on the dwell-time between switching instants. An alternative condition based on Kronecker calculus is worked out. It is shown that both the stability criteria admit an LMI implementation. 相似文献
50.
When performing a classification task, we may find some data-sets with a different class distribution among their patterns. This problem is known as classification with imbalanced data-sets and it appears in many real application areas. For this reason, it has recently become a relevant topic in the area of Machine Learning.The aim of this work is to improve the behaviour of fuzzy rule based classification systems (FRBCSs) in the framework of imbalanced data-sets by means of a tuning step. Specifically, we adapt the 2-tuples based genetic tuning approach to classification problems showing the good synergy between this method and some FRBCSs.Our empirical results show that the 2-tuples based genetic tuning increases the performance of FRBCSs in all types of imbalanced data. Furthermore, when the initial Rule Base, built by a fuzzy rule learning methodology, obtains a good behaviour in terms of accuracy, we achieve a higher improvement in performance for the whole model when applying the genetic 2-tuples post-processing step. This enhancement is also obtained in the case of cooperation with a preprocessing stage, proving the necessity of rebalancing the training set before the learning phase when dealing with imbalanced data. 相似文献