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| 1 | New directions towards structure formation and stability of protein-rich foods from globular proteins显示文摘 | Nanik Purwanti Atze Jan van der Goot Remko Boom Johan Vereijken | 2009 | Trends in Food Science & Technology2009，，2： | 1 |
| 2 | Image Inpainting using Erosion and Dilation Operation显示文摘 | Naser Jawas Nanik Suciati | 2013 | International Journal of Advanced Science and Technology2013，51，： | 1 |
| 3 | Modulation of rheological properties by heat- induced aggregation of whey protein solution 显示文摘 | Nanik Purwanti Mary Smiddy Atze Jan vander Goot | 2011 | Food Hydroeolloids2011，25，： | 1 |
| 4 | Combining MobileNetV1 and Depthwise Separable convolution bottleneck with Expansion for classifying the freshness of fish eyes显示文摘Image classification using Convolutional Neural Network(CNN)achieves optimal perfor-mance with a particular strategy.MobileNet reduces the parameter number for learning features by switching from the standard convolution paradigm to the depthwise separable convolution(DSC)paradigm.However,there are not enough features to learn for identify-ing the freshness of fish eyes.Furthermore,minor variances in features should not require complicated CNN architecture.In this paper,our first contribution proposed DSC Bottle-neck with Expansion for learning features of the freshness of fish eyes with a Bottleneck Multiplier.The second contribution proposed Residual Transition to bridge current feature maps and skip connection feature maps to the next convolution block.The third contribu-tion proposed MobileNetV1 Bottleneck with Expansion(MB-BE)for classifying the freshness of fish eyes.The result obtained from the Freshness of the Fish Eyes dataset shows that MB-BE outperformed other models such as original MobileNet,VGG16,Densenet,Nasnet Mobile with 63.21%accuracy. | Eko Prasetyo Rani Purbaningtyas Raden Dimas Adityo Nanik Suciati Chastine Fatichah | 2022 | Information Processing in Agriculture2022，9，4： | 1 |
| 5 | Contamination of microplastics in Brantas River, East Java, Indonesia and its distribution in gills and digestive tracts of fish Gambusia affinis显示文摘The Brantas River is currently vulnerable to microplastics pollution.Microplastics not only pollute the aquatic environment but also enter the body of fish and other aquatic organisms.This research is aimed at deciding if microplastics were present in the waters and the gills and digestive tract of the Gambusia affinis fish of the river.It also looked at differences in the abundance of several types of microplastics found in the various organ samples and locations.Field research was conducted from January 2020 to March 2020.The microplastics were identified by type,size,color,and the abundance of each type was calculated.The types of microplastics identified were fragments,fibres,films,and pellets.Microplastics of 0.1 mm size are predominant and formed about 76%-100%of the microplastics that were found.Black microplastics were more common in water samples(24%),gills(43%),and digestive tract(46%).The greatest abundance of microplastic fragments was found in water samples of 4066.67 particles/m^(3),1352.78 particles/gram in gill samples,and 2138.89 particles/gram in the digestive tract.Multivariate tests for variants of microplastic types found in the organs at different sampling locations gave a p-value<0.05.These results indicate a difference in the abundance values of microplastic species in different organ samples and sites. | Nanik Retno Buwono Yenny Risjani Agoes Soegianto | 2021 | Emerging Contaminants2021，7，1： | 1 |
| 6 | CARVING-DETC: A network scaling and NMS ensemble for Balinese carving motif detection method显示文摘Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs,each representing the values adopted by the Balinese people. Detection of Balinese carving motifs ischallenging due to the unavailability of a Balinese carving dataset for detection tasks, high variance,and tiny-size carving motifs. This research aims to improve carving motif detection performance onchallenging Balinese carving motifs detection task through a modification of YOLOv5 to support adigital carving conservation system. We proposed CARVING-DETC, a deep learning-based Balinesecarving detection method consisting of three steps. First, the data generation step performs dataaugmentation and annotation on Balinese carving images. Second, we proposed a network scalingstrategy on the YOLOv5 model and performed non-maximum suppression (NMS) on the modelensemble to generate the most optimal predictions. The ensemble model utilizes NMS to producehigher performance by optimizing the detection results based on the highest confidence score andsuppressing other overlap predictions with a lower confidence score. Third, performance evaluation onscaled-YOLOv5 versions and NMS ensemble models. The research findings are beneficial in conservingthe cultural heritage and as a reference for other researchers. In addition, this study proposed a novelBalinese carving dataset through data collection, augmentation, and annotation. To our knowledge,it is the first Balinese carving dataset for the object detection task. Based on experimental results,CARVING-DETC achieved a detection performance of 98%, which outperforms the baseline model. | Wayan Agus Surya Darma Nanik Suciati Daniel Siahaan | 2023 | Visual Informatics2023，7，3： | 0 |
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