• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.52 no.4
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• pp.408-417
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• 2019

This study was conducted to identify taxonomic differences in the characteristics of Rhodeus uyekii and Rhodeus ocellatus during their initial life history via an interspecific hybridization experiment. Hybrids were compared to their parent species, and the findings were used to inform developmental research in commercially useful aquarium fish. The hatching rates of the cross-bred eggs were 75.9% for cross UO (R. uyekii ♀${\times}$R. ocellatus♂) and 71.9% for cross OU (R. ocellatus♀${\times}$R. uyekii♂), which did not differ greatly from the hatching rates of the normal cross-bred group. Backcross experiments resulted in 100% egg mortality during development. Newly hatched larvae of the original hybrid crosses were similar to those of the maternal line, and the color of the egg yolk was similar to that of the paternal line; therefore, the respective traits of the interspecific parents were identifiable within the cross-bred offspring.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.36 no.3
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• pp.209-213
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• 1991

Early maturing hybrids(Comet 80, Comet 85 and Linda) and mid-late maturing hybrid (Suwon 19) were grown at Suwon to study the influence of maturity, plant population, and sowing date (45, 55, and 65 days delayed from the recommended sowing date of Suwon 19) on the dry matter yields, percentage ears and TDN yields. Com was planted at the density of 60 x 20, 50 x 20 and 40 x 20 em. Dry matter yields were not significantly different between the early and mid-late maturing hybrids in the delayed sowing time, but significantly higher in 20 x 50cm of planting density in early maturing hybrids. As sowing was delayed to Jun. 22 ear content decreased from 40.3% (early maturing hybrid) to 6.7% (Suwon 19). This resulted in significantly 24.1% higher TDN yield in Comet 85 than in Suwon 19.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.2
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• pp.170-179
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• 2021

This study was conducted to identify taxonomic differences in the characteristics of Acheilognathus majusculus and A. yamatsutae during their initial life history via an interspecific hybridization experiment. Hatching time required 36 h for MY and 49 h for YM at 21.5℃, showing a significant difference of 13 h between the hybrids. The hatching rates of the cross-bred eggs were 30% for cross MY (A. majusculus♀×A. yamatsutae♂) and 40% for cross YM (A. yamatsutae♀×A. majusculus♂). The hatching larvae size was total length 3.13-3.43 mm in MY and total length 3.89-4.22 mm in YM, which was larger in YM. The hybridization test between A. yamatsutae and A. majusculus that live in the same water stream confirmed that no interspecific reproductive isolation occurred.

• Journal of Life Science
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• v.10 no.1
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• pp.48-50
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• 2000

The cytogenetic pattern of autosome and sex chromiosome after Giemas staining were examined in the hybrids between two sibling species, Drosophila melanogaster and D. simulans. The analysis of karyotype in the hybrid female between D. melanogaster females and D. simulans males could be easily distinguished the characteriation of eight chromosomes from bothe species, especially with regard to X chromosomes. The lagging duplication of Y chromosome was investigated in the interspecific hybrid males from the cross between female of Drosophila melanogaster(OR) and males of D. simulasn (K18). On the other hand, the X chromatids of D. simulans were loosely separated in the early stage of anaphase.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.23-27
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• 2007

Many trials have been used to model room impulse responses, all attempting to provide efficient representations of room acoustics. The traditional model designs for room impulse response seem to fail in accuracy, controllability, or computational efficiency. In time domain, a room impulse response is generally considered as the combination of three parts having different acoustic characteristics, initial time delay, early reflection, and late reverberation. This paper introduces new learning hybrid model for the room impulse response. In this proposed model, those three parts are modeled using different models with learning algorithms that determine the length or boundary of each model in the hybrid model. By the simulation with measured room impulse responses, it was examined that the performance of proposed model shows the best efficiency in views of both the parameter numbers and modeling error.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.3
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• pp.361-367
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• 2008

Many trials have been used to model room impulse responses, all attempting to provide efficient representations of room acoustics. The traditional model designs for room impulse response seem to fail in accuracy, controllability, or computational efficiency. In the time domain, room impulse responses are generally considered as combination of the three Parts having different acoustic characteristics, initial time delay, early reflection, and late reverberation. This paper introduces new learning hybrid model for room impulse responses. In this proposed model, those three parts are modeled using different models with learning algorithms that determine the boundary of each model in the hybrid model. By the simulation with measured room impulse responses, the performance of proposed model shows the best efficiency in views of computational burden and modeling error.

• Journal of The Korean Society of Grassland and Forage Science
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• v.13 no.2
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• pp.132-138
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• 1993

Yield and plant constituent responses of forage sorghum cultivars have usually been compared in uniform defoliation management test. However the influence of harvest time on differential response of cultivars needs more precise definition. Therefore this study with sorghum-sorgo-sudan hybrid cv. NC+Sweet Leaf, and Super Su 22 and Sorghum-Sorghum hybrid cv. Pioneer 931 was carried out under two defoliation regimes, namely defoliation at heading stage of each variety and defoliation on the same calendar date in response to heading stage of early variety. The results are summarized as follows; 1. Three harvests were taken by early variety with 80 days and two harvests by late variety with 94 days from sowing to heading. 2. Dry matter and TDN yield tend to be higher when the plants are cut at ear emergence stage of late variety. 3. Crude protein content was similar for the same growth growth stage of 1st growth and 1st regrowth, and rather big different between varieties. 4. Considering only dry matter and TDN yield, it is recommendable to cut two times at ear emergence stage of late variety and also three times at ear emergence stage of early variety in view point of utilization period extension and distribution of forage products.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.93-105
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• 2006

The domination of the Internet by TCP-based services has spawned many efforts to provide high network utilization with low loss and delay in a simple and scalable manner. Active queue management (AQM) algorithms attempt to achieve these goals by regulating queues at bottleneck links to provide useful feedback to TCP sources. While many AQM algorithms have been proposed, most suffer from instability, require careful configuration of nonintuitive control parameters, or are not practical because of slow response to dynamic traffic changes. In this paper, we propose a new AQM algorithm, hybrid random early detection (HRED), that combines the more effective elements of recent algorithms with a random early detection (RED) core. HRED maps instantaneous queue length to a drop probability, automatically adjusting the slope and intercept of the mapping function to account for changes in traffic load and to keep queue length within the desired operating range. We demonstrate that straightforward selection of HRED parameters results in stable operation under steady load and rapid adaptation to changes in load. Simulation and implementation tests confirm this stability, and indicate that overall performances of HRED are substantially better than those of earlier AQM algorithms. Finally, HRED control parameters provide several intuitive approaches to trading between required memory, queue stability, and response time.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.321-339
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• 2021

Response Surface Method (RSM) has been widely used for flammable cloud size prediction as it can reduce computational intensity for further Explosion Risk Analysis (ERA) especially during the early design phase of offshore platforms. However, RSM encounters the overfitting problem under very limited simulations. In order to overcome the disadvantage of RSM, Bayesian Regularization Artificial Neural (BRANN)-based model has been recently developed and its robustness and efficiency have been widely verified. However, for ERA during the early design phase, there seems to be room to further reduce the computational intensity while ensuring the model's acceptable accuracy. This study aims to develop an integrated method, namely the combination of Center Composite Design (CCD) method with Bayesian Regularization Artificial Neural Network (BRANN), for flammable cloud size prediction. A case study with constant and transient leakages is conducted to illustrate the feasibility and advantage of this hybrid method. Additionally, the performance of CCD-BRANN is compared with that of RSM. It is concluded that the newly developed hybrid method is more robust and computational efficient for ERAs during early design phase.