• International Journal of Computer Science & Network Security
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• 제22권10호
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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.

• Structural Engineering and Mechanics
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• 제63권5호
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• pp.607-615
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• 2017

This study presents optimum design of plane steel bridges considering corrosion effect by using teaching-learning based optimization (TLBO) method. Optimum solutions of three different bridge problems are linearly carried out including and excluding corrosion effect. The member cross sections are selected from a pre-specified list of 128 W profiles taken from American Institute of Steel Construction (AISC). A computer program is coded in MATLAB to carry out optimum design interacting with SAP2000 using OAPI (Open Application Programming Interface). The stress constraints are incorporated as indicated in AISC Allowable Stress Design (ASD) specifications and also displacement constraints are applied in optimum design. The results obtained from analysis show that the corrosion effect on steel profile surfaces causes a crucial increase on the minimum steel weight of bridges. Moreover, the results show that the method proposed is applicable and robust to reach the destination even for complex problems.

• Smart Structures and Systems
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• 제24권2호
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• pp.233-242
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• 2019

In this paper, an inverse approach based on uniform load surface (ULS) is presented for structural damage localization and quantification. The ULS is excellent approximation for deformed configuration of a structure under distributed unit force applied on all degrees of freedom. The ULS make use of natural frequencies and mode shapes of structure and in mathematical point of view is a weighted average of mode shapes. An objective function presented to damage detection is discrepancy between the ULS of monitored structure and numerical model of structure. Solving this objective function to find minimum value yields damage's parameters detection. The teaching-learning based optimization algorithm has been employed to solve inverse problem. The efficiency of present damage detection method is demonstrated through three numerical examples. By comparison between proposed objective function and another objective function which make use of natural frequencies and mode shapes, it is revealed present objective function have faster convergence and is more sensitive to damage. The method has good robustness against measurement noise and could detect damage by using the first few mode shapes. The results indicate that the proposed method is reliable technique to damage detection in structures.

• Steel and Composite Structures
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• 제28권4호
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• pp.509-516
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• 2018

The differential quadrature (DQ) and teaching-learning based optimization (TLBO) methods are coupled to introduce a hybrid numerical method for maximizing fundamental natural frequency of laminated composite skew plates. The fiber(s) orientations are selected as design variable(s). The first-order shear deformation theory (FSDT) is used to obtain the governing equations of the plate. The equations of motion and the related boundary conditions are discretized in space domain by employing the DQ method. The discretized equations are transferred from the time domain into the frequency domain to obtain the fundamental natural frequency. Then, the DQ solution is coupled with the TLBO method to find the maximum frequency of the plate and its related optimum stacking sequences of the laminate. Convergence and applicability of the proposed method are shown and the optimum fundamental frequency parameter of the plates with different skew angle, boundary conditions, number of layers and aspect ratio are obtained. The obtained results can be used as a benchmark for further studies.

• Steel and Composite Structures
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• 제33권5호
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• pp.747-753
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• 2019

Metaheuristic algorithm is used to solve the weight minimization problem of truss structures considering shape, and sizing design variables. The cross-sectional areas of the line element in trusses are the design variables for size optimization and the changeable joint coordinates are the shape optimization used in this study. The design of plane and spatial truss structures are optimized by metaheuristic technique named Teaching-Learning-Based Optimization (TLBO). Finite element analyses of structures and optimization process are carried out by the computer program visually developed by the authors coded in MATLAB. The four benchmark problems (trusses 2D ten-bar, 3D thirty-seven-bar, 3D seventy-two-bar and 2D two-hundred-bar) taken from literature are optimized and the optimal solution compared the results given by previous studies.

• Structural Engineering and Mechanics
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• 제71권2호
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• pp.139-151
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• 2019

In a project schedule, it is possible to reduce the time required to complete a project by allocating extra resources for critical activities. However, accelerating a project causes additional expense. This issue is addressed by finding optimal set of time-cost alternatives and is known as the time-cost trade-off problem in the literature. The aim of this study is to identify the optimal set of time-cost alternatives using a multiobjective teaching-learning-based optimization (TLBO) algorithm integrated with the non-dominated sorting concept and is applied to successfully optimize the projects ranging from a small to medium large projects. Numerical simulations indicate that the utilized model searches and identifies optimal / near optimal trade-offs between project time and cost in construction engineering and management. Therefore, it is concluded that the developed TLBO-based multiobjective approach offers satisfactorily solutions for time-cost trade-off optimization problems.

• Structural Engineering and Mechanics
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• 제87권6호
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• pp.555-574
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• 2023

An efficient optimization algorithm and damage-sensitive objective function are two main components in optimization-based Finite Element Model Updating (FEMU). A suitable combination of these components can considerably affect damage detection accuracy. In this study, a new hybrid damage-sensitive objective function is proposed based on combining two different objection functions to detect the location and extent of damage in structures. The first one is based on Generalized Pseudo Modal Strain Energy (GPMSE), and the second is based on the element's Generalized Flexibility Matrix (GFM). Four well-known population-based metaheuristic algorithms are used to solve the problem and report the optimal solution as damage detection results. These algorithms consist of Cuckoo Search (CS), Teaching-Learning-Based Optimization (TLBO), Moth Flame Optimization (MFO), and Jaya. Three numerical examples and one experimental study are studied to illustrate the capability of the proposed method. The performance of the considered metaheuristics is also compared with each other to choose the most suitable optimizer in structural damage detection. The numerical examinations on truss and frame structures with considering the effects of measurement noise and availability of only the first few vibrating modes reveal the good performance of the proposed technique in identifying damage locations and their severities. Experimental examinations on a six-story shear building structure tested on a shake table also indicate that this method can be considered as a suitable technique for damage assessment of shear building structures.

• Geomechanics and Engineering
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• 제31권4호
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• 정보교육학회논문지
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• 제9권1호
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• pp.79-86
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• 2005

컴퓨터와 통신 기술이 발전함에 따라 네트워크를 통한 일반 사용자들의 컴퓨터 활용 빈도와 요구하는 데이터의 양이 급격히 증가되었다. 이에 따라 최근의 교육 시스템들은 정보의 활용성을 향상시키기 위하여 이질적인 시스템들을 의미상으로 연결하고 있다. 따라서 최근의 웹 기반 교수-학습은 학습자 스스로 학습 내용, 학습 시간 및 학습 순서를 선택하고 조직하는 유비쿼터스 학습방향으로 나아가고 있다. 즉, 학습자 개개인의 특성(선수 지식, 학습 양식, 흥미, 관심)에 맞는 적응적인 교수-학습 환경을 제공하는 방향으로 변화되고 있다. 본 논문은 유비쿼터스 학습 환경에서 다양한 분산정보의 통합을 위하여 사용자들이 요구하는 학습내용을 각 지역서버의 자치성을 유지하면서 효과적으로 학습하기 위한 미디에이터내의 처리방법에 대해 제안한다. 또한 과거와 최근의 학습내용의 활용형태가 다양하게 변할 수 있으므로 시간에 따른 감쇄율을 활용빈도에 적용하여 최근의 활용빈도의 변화에 민감하게 반응하고 활용형태의 변화에 따라 적응적으로 학습내용을 사용할 수 있는 방법을 제안한다.

• 정보교육학회논문지
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• 제21권6호
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• pp.629-638
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• 2017

정보 교과에서 컴퓨팅사고력을 통해 해결하고자 하는 정보 교과 문제의 정의와 특성을 알아보고, 정보 교과 문제의 유형과 사례, 정보 교과 문제 해결을 위한 교수학습방법 등에 대하여 탐구하였다. 정보 교과 문제에 대해 알아보기 전, 먼저 타 교과에서 문제의 정의와 문제의 종류, 문제 해결의 중요성에 대하 조사하였다. 이러한 조사를 바탕으로 정보 교과는 컴퓨팅사고력과 컴퓨팅 파워를 통해 비구조화된 문제를 해결할 수 있고, 그러한 문제에는 카운팅 문제, 결정 문제, 검색 문제, 최적화 문제 등이 있다는 것을 알게 되었다. 이러한 컴퓨팅 과학 문제를 해결하기 위해 알고리즘 기반의 교수학습 방법, 즉, 탐색기반 교수학습방법과 관계기반 교수학습방법을 제안하였다. 선생님들은 문제의 특성에 따라 두 가지 방법을 활용함으로써, 학생들이 문제를 해결하는 과정에서 추상화, 자동화, 일반화 등의 컴퓨팅사고력을 향상시킬 수 있다.