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The main objective of this study is to measure the relative efficiency of Indonesian universities in 2015. There are twenty five DMUs with four inputs and five outputs that are analyzed. Due to the low number of Indonesian scientific publications, this study analyses the performance of the top 25 universities based on the Webometrics ranking as it has been used as one of the indicators of university achievements by the Higher Education of Indonesia. The Data Envelopment Analysis (DEA) is used to obtain the scores of efficiency, while the Fuzzy approach is applied to address the possibility of errors from the auditor's assessment in determining the input and output variables correctly. The FDEA can be used in measuring the universities performances under imprecise inputs and outputs. Both the CRS (constant returns to scale) and the VRS (variable returns to scale) models are presented. The empirical results show that 36 percent of universities perform efficiently on the CRS model while 52 percent of universities have efficient performances under the VRS model. Furthermore, the well-known universities have shown relatively low scores, which indicate they need to improve their performances in publishing scientific work, as well as providing useful information to the public through the official websites. Generally, the results of the VRS model are better than the CRS model for both the DEA and the FDEA methods.

Atrophic gastritis (AG) is commonly caused by the infection of the Helicobacter pylori (H. pylori) bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on H. pylori-associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect H. pylori infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.

The Logistic Regression Model (LRM) is successful in many fields due to its capability of predicting and describing the relationship between binary response variables and one or more independent variables. However, the prediction results of this model are still not accurate enough due to error terms, regardless of their existence in the model. To overcome this problem and, at the same time, produce more accurate and efficient predictive model values, the bootstrap approach was proposed. Unfortunately, this approach did not receive any attention, especially for this model. This study aims to introduce the bootstrap approach to LRM and investigate the performance of the proposed models using data on wound healing using jellyfish collagen. The results revealed that the proposed model generated smaller values of MSE and RMSE, as well as shorter confidence intervals, compared with the existing LRM. These results proved that the proposed model could produce an estimated value that is more accurate and efficient than those of the LRM. The results warrant a proper ecosystem management for the perpetual medicinal use and conservation of jellyfish, which is also related to the productive resources and services target by 2030 for SDG 14 involving marine life.

In this paper, a system dynamics approach is used instead of the traditional approaches to stimulate, forecast and analyze the economic effects of an existing policy practice in Setiu Wetland. As a part of Setiu district that uphold tradition in fishery and maritime based industry, Setiu Wetland area seems to be left behind in terms of economic and livelihood. Generally, Setiu development policy consists of five subsystem including population growth, economic, residential, transportation and suburban sprawl. Due to their widespread population distribution, Setiu Wetland receives low urban-related progress. Hence, a forecast of 30 years from 2016 to 2046 providing a necessary insight for potential development of the Setiu Wetland region, to simulate its environment, identify gaps, propose suitable land model towards Setiu Minapolitan area (Peri-urban area) and suggest directions for future studies particularly in economic and livelihood for local authorities to develop with.

This study estimates the efficiency of employees' performances under profit sharing system using data envelopment analysis (DEA). This method is one of the most common methods used in efficiency measurement analysis. However, a robust approach is used to deal with the complexity of the traditional DEA estimators. Robust Data Envelopment Analysis (RDEA) is very useful when outliers contaminate the data. The sample includes five divisions which cover as many as 102 employees of a shipping company in Malaysia are analyzed by using R program. The results reveal that the initial DEA efficiency is an over-estimate of the true efficiency. RDEA provides better accuracy of the results. Further, the robust approach is appropriate to be used in the measurement of the efficiency of company divisions under profit sharing program.