Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Fig. 1 in Alien hitchhiker insect species detected from the international vessels entering into Korea in 2021

Fig. 1. Body photos on detected individuals of the alien hitchhiking insect species from the international vessels. A. Chondracris rosea (Acrididae, Orthoptera); B. Mecopoda elongata (Tettigoniidae, ditto); C. Epidaus famulus (Reduviidae, Hemiptera); D. Poecilocoris druraei (Scutelleridae, ditto); E, F. Neochauliodes meridionalis (Corydalidae, Megaloptera); G. Calathus fuscipes (Carabidae, Coleoptera); H. Odontolabis cuvera (Lucanidae, ditto); I. Eriopis chilensis (Coccinellidae, ditto); J. Sagra femorata (Chrysomelidae, ditto); K. Anoplophora horsfieldii (Cerambycidae, ditto); L. Cryptocheilus australis (Pompilidae, Hymenoptera); M. Atta colombica (Formicidae, ditto); N. Eucera nigrescens (Apidae, ditto); O. Palpita quadristigmalis (Crambidae, Lepidoptera).

opencc-by-4.0Dec 2023View details →

Fig. 2 in Alien hitchhiker insect species detected from the international vessels entering into Korea in 2021

Fig. 2. Body photos on detected individuals of the alien hitchhiking insect species from the international vessels. A. Arippara disticha (Pyralidae, Lepidoptera); B, C. Blenina donans (Nolidae, ditto); D. Gadirtha fusca (Nolidae, ditto); E. Chloroclystis pyrrholopha (Geomet- ridae, ditto); F. Achaea serva (Erebidae, ditto); G. Anomis combinans (Erebidae, ditto); H. Eudocima procus (Erebidae, ditto); I. Laspeyria subrosea (Erebidae, ditto); J. Mimophisma delunaris (Erebidae, ditto); K, L. Euhampsonia serratifera (Notodontidae, ditto); M. Dendrolimus punctatus (Lasiocampidae, ditto); N. Clanis stenosema (Sphingidae, ditto).

Figure 5 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts

Figure 5. Phylogenetic tree based on 16S rRNA sequences of Cardinium, constructed by a neighbor-joining procedure. Cardinium strains are depicted by the host name. The accession numbers are shown after the host name. Numbers on the nodes indicate bootstrap percent confidence values.

Figure 2 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts

Figure 2. Neighbor-joining tree of COI sequences of the Erythraeidae mites of the present study and the Genbank sequence data. Numbers above/below nodes represent bootstrap values.

Figure 1 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts

Figure 1. Erythraeus (Erythraeus) pistacicus Haitlinger, Mehrnejad & Šundić, 2016 larva (Black arrow)inside the gall, feeding on the aphid, Forda hirsuta Mordvilko, 1928, on pistachio trees. June 2022, Mashhad, Northeast of Iran.

Figure 7 in New host detection of the parasitic mite, Erythraeus pistacicus (Trombidiformes: Erythraeidae) from Iran and indication of possible infection with bacterial symbionts

Figure 7. Phylogenetic relationship of Planomicrobium symbiont identified from Erythraeus (Erythraeus) pistacicus with related sequences retrieved from GenBank. The tree was constructed using neighbor-joining procedure. The sequence obtained from E. (E.) pistacicus in this study is in red box. Sequence from Bacillus subtilis was used as an

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 11. Activated Neuro-Symbols for Detecting that a Person walks around in the Room

With the example of Figure 11, also the function of feedback connections can be explained. According to the existing feedforward connections, the neuro-symbol “object stands” would be activated together with the neuro-symbol “object moves” whenever the neuro-symbols “motion” and “object moves” are active, because it is activated by a subset of the neuro-symbols that activate the neuro-symbol “object moves”. This activation would however be undesired in this concrete case. For this reason, an inhibitory feedback connection exists from the neuro-symbol “object moves” to the neuro-symbol “object stands” that inhibits the activation of the neuro-symbol “object stands”.

Figure 2. Overall process of the system -An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

This paper mainly focuses on automated detection of White Matter Lesions of brain using fast and efficient clustering algorithms. The goal of clustering a medical image is to simplify the representation of an image into a meaningful image and makes it easier to analyze. As a first step, MRI brain image is pre-processed using Contrast Stretching technique which is one of the efficient image enhancement techniques. The pre-processed image is subjected to clustering. The clustering algorithms include Fuzzy c-means Clustering (FCM), Geostatistical Possibilistic Clustering (GPC) and Geostatistical Fuzzy Clustering Model (GFCM). However clustering techniques are sensitive to initialization and are easily trapped in local optima. In order to obtain an optimized result, the clustered images are undergone optimization. Particle swarm optimization (PSO) is a stochastic global optimization tool which is used in many optimization problems. Figure 2 represents overall process of automatic detection of WMLs of brain. Since MS lesions present different characteristics from lesions in elderly individuals there are many clustering models to determine the accuracy but those methods are not directly applicable to predict the accurate lesions because of the decreased contrast between White Matter and Grey Matter in elderly people. The proposed clustering models are derived by extending the objective functions of FCM and Possibilistic clustering with a Geostatistical (spatial) model. These algorithms are applied to real magnetic resonance images and is shown to be more robust to noise and other artifacts than competing approaches.

Figure 9. Performance analysis of FCM-PSO, GPC-PSO and GFCM-PSO-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

Figure 8. Performance analysis of FCM, GPC and GFCM Figure 9.-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

All scans obtained from different image clustering models are manually ranked based on values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO) are applied on a dataset of 208 images and ranking is done in terms of under detected, over detected, properly detected as shown in figure 8 and figure 9. The number of images detected properly in GFCM is comparatively high than FCM and GPC. The optimized result of GFMC provides accurate detection of WMLs and it properly detects 195 images.

Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

The performance of WML quantification is evaluated using clustering algorithms. When the image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is clustered using the effective clustering algorithms. Figure 3 represents the input image for WML detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4 depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to clustering. Three clustering models are proposed to provide accurate results. All scans obtained from different image clustering models are manually ranked based on values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO) are applied on a dataset of 208 images and ranking is done in terms of under detected, over detected, properly detected as shown in figure 8 and figure 9. The number of images detected properly in GFCM is comparatively high than FCM and GPC.

Figures a, b ,c -8-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

In turn, the optimized results of GFCM provide overall accuracy of 95%. Figures 8(a), 8(b) and 8(c) shows the comparison results of clustering models and optimization technique in terms of Se, Sp and Acc.

opencc-by-4.0Jan 2018View details →

ODDS: Real-Time Object Detection using Depth Sensors on Embedded GPUs

ODDS Smart Building Depth Dataset #Introduction: The goal of this dataset is to facilitate research focusing on recognizing objects in smart buildings using the depth sensor mounted at the ceiling. This dataset contains annotations of depth images for eight frequently seen object classes. The classes are: person, backpack, laptop, gun, phone, umbrella, cup, and box. #Data Collection: We collected data from two settings. We had Kinect mounted at a 9.3 feet ceiling near to a 6 feet wide door. We also used a tripod with a horizontal extender holding the kinect at a similar height looking downwards. We asked about 20 volunteers to enter and exit a number of times each in different directions (3 times walking straight, 3 times walking towards left side, 3 times walking towards right side) holding objects in many different ways and poses underneath the Kinect. Each subject was using his/her own backpack, purse, laptop, etc. As a result, we considered varieties within the same object, e.g., for laptops, we considered Macbooks, HP laptops, Lenovo laptops of different years and models, and for backpacks, we considered backpacks, side bags, and purse of women. We asked the subjects to walk while holding it in many ways, e.g., for laptop, the laptop was fully open, partially closed, and fully closed while carried. Also, people hold laptops in front and side of their bodies, and underneath their elbow. The subjects carried their backpacks in their back, in their side at different levels from foot to shoulder. We wanted to collect data with real guns. However, bringing real guns to the office is prohibited. So, we obtained a few nerf guns and the subjects were carrying these guns pointing it to front, side, up, and down while walking. #Annotated Data Description: The Annotated dataset is created following the structure of Pascal VOC devkit, so that the data preparation becomes simple and it can be used quickly with different with object detection libraries that are friendly to Pascal VOC style annotations (e.g. Faster-RCNN, YOLO, SSD).  The annotated data consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the eight classes present in the image. Multiple objects from multiple classes may be present in the same image. The dataset has 3 main directories: 1)DepthImages: Contains all the images of training set and validation set.  2)Annotations: Contains one xml file per image file, (e.g., 1.xml for image file 1.png). The xml file includes the bounding box annotations for all objects in the corresponding image.  3)ImagesSets: Contains two text files training_samples.txt and testing_samples.txt. The training_samples.txt file has the name of images used in training and the testing_samples.txt has the name of images used for testing. (We randomly choose 80%, 20% split) #UnAnnotated Data Description: The un-annotated data consists of several set of depth images. No ground-truth annotation is available for these images yet. These un-annotated sets contain several challenging scenarios and no data has been collected from this office during annotated dataset construction. Hence, it will provide a way to test generalization performance of the algorithm. #Citation: If you use ODDS Smart Building dataset in your work, please cite the following reference in any publications: @inproceedings{mithun2018odds, title={ODDS: Real-Time Object Detection using Depth Sensors on  Embedded GPUs}, author={Niluthpol Chowdhury Mithun and Sirajum Munir and Karen Guo and Charles Shelton}, booktitle={ ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN)}, year={2018}, }

opencc-by-4.0Jan 2018View details →

Research data supporting "MicroRNA Detection by DNA-Mediated Liposome Fusion"

Raw research data supporting the publication: Jumeaux C., et al., 2017, "MicroRNA detection by DNA-mediated liposome fusion", ChemBioChem DOI: 10.1002/cbic.201700592

opencc-by-4.0Feb 2018View details →

Synthetic Dataset for Outlier Detection

This synthetically generated dataset can be used to evaluate outlier detection algorithms. It has 10 attributes and 1000 observations, of which 100 are labeled as outliers. Two-dimensional combinations of attributes form differently shaped clusters. <ul> <li>Attribute 0 & Attribute 1: Two circular clusters</li> <li>Attribute 2 & Attribute 3: Two banana shaped clusters</li> <li>Attribute 4 & Attribute 5: Three point clouds</li> <li>Attribute 6 & Attribute 7: Two point clouds with variances</li> <li>Attribute 8 & Attribute 9: Three anisotropic shaped clusters. </li> </ul> The "outlier" column states whether an observation is an outlier or not. Additionally, the .zip file contains 10 stratified randomized train test splits (70% train, 30% test).

opencc-by-4.0Aug 2016View details →

BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 5.The chart of run time in emotional/unemotional detection in two databases.

The processing time of our proposed method is of 0.420468 fps and in regular data is of 5.27 fps. As the results show, the EVM method has good effect in processing. Of course, the creation time of the magnification image sequence was not considered in the result. In other words, the processing time on a magnified images sequence is less than the non-magnification state. Figure 5 shows the running time in emotional/unemotional detection.

opencc-by-4.0Aug 2016View details →

BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 4. The chart of average percent of emotional/unemotional detection in SMIC

In Figure 4 the blue color curve represents the average percent of emotional/unemotional detection using the EVM method. The red color curve also represents the average percent of emotional/unemotional detection without this method. Table 1 shows the running time and speed in this experiment.

opencc-by-4.0Aug 2016View details →

BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 3.The chart of emotional/unemotional detection on the face in negative, positive and surprise states (Regular and magnified data)

To evaluate the emotional/unemotional detection on the face, 328 tests were performed: 164 tests on the magnified data and 164 tests on the regular data. For this purpose, the train set includes the neutral state and only one of the emotional states (negativism, positivism and surprise) according to the test set. So that the train set includes regular data in 328 experiments. The experimental results are shown in Figure 3.

opencc-by-4.0Jun 2016View details →

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 19. Accuracy of classification using the three methods: KNN, SVM and our method for MCI subjects

Whatever the patient condition, Normal, MCI or AD, our method has provided us with better results. Advocate Example precision for Normal Patients was found 96% as opposed to 88% for the SVM method and 84% for KNN. For MCI patients was found 88% as opposed to 80% for the SVM method and 72% for KNN. Also for AD patients were found 92% as opposed to 88% for the SVM method and 80% for KNN. Our classification method gave us the best results, finding overall accuracy of 92% as opposed to 84% for the SVM method and 78.66% for KNN.