• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.327-338
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• 2013

Effective and efficient selection of IT projects is crucial for company's competitiveness. The selection of IT projects usually involves consideration of budget constraints but existing IT project selection models often neglect budget constraints. This paper presents an IT project selection model which considers budget constraints. AHP(Analytic Hierarchy Process) and Knapsack problem model have been combined to develop the proposed model, AHP-K model, where AHP is used to estimate weights of selection criteria and, then, a knapsack problem model is utilized to optimize selection of IT project while meeting the budget constraints. In this paper, a case study is provided to validate the effectiveness of the proposed AHP-K model. It has been shown that the proposed AHP-K model is better than the AHP model in terms of total utility of projects and investment efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• Journal of Information Technology Applications and Management
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• v.19 no.3
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• pp.19-30
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• 2012

Material selection is a complex problem in the design and development of products for diverse engineering applications. This paper is aimed to present a fuzzy decision making approach to deal with the material selection in engineering design problems. A fuzzy multi criteria decision-making model is proposed for solving the material selection problem. The proposed model makes use of fuzzy TOPSIS (Technique for Order reference by Similarity to Ideal Solution) with trapezoidal numbers for evaluating the criteria and ranking the alternatives. And result is compared with fuzzy VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian, means Multi criteria Optimisation and Compromise Solution) which is proposed by Jeya Girubha and Vinodh [2012]. The present paper is aimed to also improve literature of fuzzy decision making for material selection problem.

• Journal of the Korea Institute of Building Construction
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• v.11 no.5
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• pp.523-529
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• 2011

In a tall building construction with reinforced concrete structures, the selection of an appropriate formwork system is a crucial factor for the success of the project. Thus, selecting an appropriate formwork system affects the entire construction duration and cost, as well as subsequent construction activities. However, in practice, the selection of an appropriate formwork system has depended mainly on the intuitive and subjective opinion of working level employees with restricted experience. Therefore, in this study, a formwork system selection model using the Adaboost algorithm is proposed to support the selection of a formwork system that is suitable for the construction site conditions. To validate the applicability of the proposed model, the selection models Adaboost and ANN were both applied to actual case data of tall building construction in Korea. The Adaboost model showed slightly better accuracy than that of the ANN model. The Adaboost model can assist engineers to determine the appropriate formwork system at the inception of future projects.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• The Plant Pathology Journal
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• v.29 no.2
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• pp.144-153
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• 2013

Diverse bacteria are known to colonize plants. However, only a small fraction of that diversity has been evaluated for their biopesticide potential. To date, the criteria for sampling and selection in such bioprospecting endeavors have not been systematically evaluated in terms of the relative amount of diversity they provide for analysis. The present study aimed to enhance the success of bioprospecting efforts by increasing the diversity while removing the genotypic redundancy often present in large collections of bacteria. We developed a multivariate sampling and marker-based selection strategy that significantly increase the diversity of bacteria recovered from plants. In doing so, we quantified the effects of varying sampling intensity, media composition, incubation conditions, plant species, and soil source on the diversity of recovered isolates. Subsequent sequencing and high-throughput phenotypic analyses of a small fraction of the collected isolates revealed that this approach led to the recovery of over a dozen rare and, to date, poorly characterized genera of plant-associated bacteria with significant biopesticide activities. Overall, the sampling and selection approach described led to an approximately 5-fold improvement in efficiency and the recovery of several novel strains of bacteria with significant biopesticide potential.

• The Journal of Information Systems
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• v.28 no.2
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• pp.25-52
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• 2019

Purpose Despite the recent increase in the use of selection tools using artificial intelligence (AI), far less is known about the effectiveness of them in recruitment and selection research. Design/methodology/approach This paper tests the impact of AI-based initial screening and interview on intentions to apply. We also examine the moderating role of individual difference (i.e., reliability on technology) in the relationship. Findings Using policy-capturing with undergraduate students at a large university in South Korea, this study showed that AI-based interview has a negative effect on intentions to apply, where AI-based initial screening has no effect. These results suggest that applicants may have a negative feeling of AI-based interview, but they may not AI-based initial screening. In other words, AI-based interview can reduce application rates, but AI-based screening not. Results also indicated that the relationship between AI-based initial screening and intentions to apply is moderated by the level of applicant's reliability on technology. Specifically, respondents with high levels of reliability are more likely than those with low levels of reliability to apply for firms using AI-based initial screening. However, the moderating role of reliability was not significant in the relationship between the AI interview and the applying intention. Employing uncertainty reduction theory, this study indicated that the relationship between AI-based selection tools and intentions to apply is dynamic, suggesting that organizations should carefully manage their AI-based selection techniques throughout the recruitment and selection process.

• Animal Bioscience
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• v.34 no.8
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• pp.1303-1308
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• 2021

Objective: The objective of this study was to define a new composite trait for Holstein dairy cows and evaluate the possibility of joint improvement in milk and fertility traits. Methods: A data set consisting 35,882 fertility related records (days open [DO], calving interval [CI], and number of services per conception [NSC], and total milk yield in each lactation [TMY]) was collected from 1998 to 2016 in Polish Holstein-Friesian breed herds. In this study TMY, DO, CI, and lactation length of each cow was used to obtain composite milk and fertility traits (CMF). Results: Moderate heritability (0.15) was estimated for composite trait that was higher than heritability of female fertility related traits: DO 0.047, CI 0.042, and NSC 0.014, and slightly lower than heritability of TMY 0.19. Favourable genetic correlations (-0.87) were estimated between CMF with TMY. Spearman rank correlation coefficients between breeding value of CMF with DO, CI, and TMY were high (>0.94) but with NSC were moderate (0.64). Selection on CMF caused favourable correlated genetic gains for DO, CI, and TMY. Different selection indices with different emphasis on fertility and milk production were constructed. The amount of correlated genetic gains obtained for DO and total milk production according to selection in CMF were higher than of genetic gains obtained for DO and TMY in selection indices with different emphasis on milk and fertility. Conclusion: The animal selection only based on a composite trait - CMF proposed in current study would simultaneously lead to favourable genetic gains for both milk and fertility related traits. In this situation CMF introduced in current study can be used to overcome to limitations of selection index and CMF could be useful for countries that have problems in recording traits, especially functional traits.

• Journal of Microbiology and Biotechnology
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• v.15 no.5
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• pp.1022-1027
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• 2005

Based on plasmid display technology by the complexes of fusion protein and the encoding plasmid DNA, an in vitro selection method for high affinity DNA-binding protein was developed and experimentally demonstrated. The GAL4 DNA-binding domain (GAL4 DBD) was selected as a model DNA-binding protein, and enhanced green fluorescent protein (EGFP) was used as an expression reporter for the selection of target proteins. Error prone PCR was conducted to construct a mutant library of the model. Based on the affinity decrease with increased salt concentration, mutants of GAL4 DBD having high affinity were selected from the mutant protein library of protein-encoding plasmid complex by this method. Two mutants of (Lys33Glu, Arg123Lys, Ile127Lys) and (Ser47Pro, Ser85Pro) having high affinity were obtained from the first generation mutants. This method can be used for rapid in vitro selection of high affinity DNA-binding proteins, and has high potential for the screening of high affinity DNA-binding proteins in a sequence-specific manner.

• Journal of Microbiology and Biotechnology
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• v.17 no.6
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• pp.993-1001
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• 2007

The technique of serological analysis of antigens by recombinant cDNA expression library (SEREX) uses autologous patient sera as a screening probe to isolate tumor-associated antigens for various tumor types. Isolation of tumor-associated antigens that are specifically reactive with patient sera, but not with normal sera, is important to avoid false-positive and autoimmunogenic antigens for the cancer immunotherapy. Here, we describe a selection methodology to isolate patient sera-specific antigens from a yeast surface-expressed cDNA library constructed from 15 patient lung tissues with non-small cell lung cancer (NSCLC). Several rounds of positive selection using patient sera alone as a screening probe isolated clones exhibiting comparable reactivity with both patient and normal sera. However, the combination of negative selection with allogeneic normal sera to remove antigens reactive with normal sera and subsequent positive selection with patient sera efficiently enriched patient sera-specific antigens. Using the selection methodology described here, we isolated 3 known and 5 unknown proteins, which have not been isolated previously, but and potentially associated with NSCLC.