Dronecaps : recognition of human actions in drone videos using capsule networks with binary volume comparisons

Understanding human actions from videos captured by drones is a challenging task in computer vision due to the unfamiliar viewpoints of individuals and changes in their size due to the camera’s location and motion. This work proposes DroneCaps, a capsule network architecture for multi-label human action recognition (HAR) in videos captured by drones. DroneCaps uses features computed by 3D convolution neural networks plus a new set of features computed by a novel Binary Volume Comparison layer. All these features, in conjunction with the learning power of CapsNets, allow understanding and abstracting the different viewpoints and poses of the depicted individuals very efficiently, thus improving multi-label HAR. The evaluation of the DroneCaps architecture’s performance for multi-label classification shows that it outperforms state-of-the-art methods on the Okutama-Action dataset

Action recognition and tracking using capsule networks

Capsule Neural Networks (CapsNets) are new deep neural networks that build hierarchical relationships between objects and their parts. The new architecture finds agreements between low-level and high-level features with the different layers of the network. Unlike neurons in Convolutional Neural Networks (CNNs), CapsNets use a capsule as the building block of the network. Each capsule is a group of neurons that capture spatial input features. When sending activation from one layer to the next layer, CapsNets send votes from the low-level capsule to the high-level capsule when they find an agreement between the coordinate frame of the two capsules. In this thesis, we study the performance of CapsNets on Human Action Recognition (HAR) and single object tracking (SOT) tasks. We proposed simple Spatial ActionCaps architecture with dynamic routing to recognise action from the Spatial dimension. To overcome the sensitivity of the CapsNets, we proposed a weight pooling algorithm to reduce the extracted features’ dimensionality and background noise. Our proposed architecture outperformed a baseline CNNs architecture. In addition, we showed the ability of the CapsNets to encode action’s temporal information in the class feature vector. We tested Spatio-Temporal CapsNets on videos captured by drone. The proposed CapsNets architecture with EM routing was able to recognise actions from unfamiliar viewpoints. Instead of weight pooling, we introduced Binary Volume Comparison (BVC) layer to reduce the noise from the 3D features. To evaluate the results of our architecture, we used four metrics for multi-label HAR. Our proposed architecture outperformed multiple CNNs methods on multi-label classes of the Okutama-Action dataset. In addition, we proposed multi-modality CapsNets for single object tracking (SOT) tasks. The proposed architecture showed faster generalization compared with a baseline CNNs SOT network. The proposed routing algorithm finds agreements between the object in the bounding box of the first frame and the remaining video frames. Based on the background and foreground classification, the coarse location of the object is located. Centreness and Regression networks help the network precisely locate the object in the remaining frames

Resistance development to bioinsecticides in Aedes aegypti (Culicidae: Diptera), the vector of dengue fever in Saudi Arabia

A laboratory strain of Aedes aegypti (L) was subjected repeatedly to larval selection pressure with two bacterial insecticides, spinosad (Saccharopolyspora spinosa) and bacilod (Bacillus thuringiensis israelensis). The results indicated that the mosquito Ae. aegypti acquired low resistance to spinosad and bacilod by about 3.1 and 2.4-fold, respectively, due to selection pressure for fifteen successive generations. The slope values of the selected strains were increased gradually from one generation to the next, indicating moderate homogeneity between individuals in their response to the test bio-insecticide. Moreover, larval selection with current bacterial bioinsecticides prolonged the time required to digest a blood meal. It showed an evident decrease in the reproductive potential of adult mosquitoes surviving selected larvae

Learning temporal information from spatial information using CapsNets for human action recognition

Capsule Networks (CapsNets) are recently introduced to overcome some of the shortcomings of traditional Convolutional Neural Networks (CNNs). CapsNets replace neurons in CNNs with vectors to retain spatial relationships among the features. In this paper, we propose a CapsNet architecture that employs individual video frames for human action recognition without explicitly extracting motion information. We also propose weight pooling to reduce the computational complexity and improve the classification accuracy by appropriately removing some of the extracted features. We show how the capsules of the proposed architecture can encode temporal information by using the spatial features extracted from several video frames. Compared with a traditional CNN of the same complexity, the proposed CapsNet improves action recognition performance by 12.11% and 22.29% on the KTH and UCF-sports datasets, respectively

Open access publishing The Journal of Diabetic Foot Complications 40 Risk factors for diabetic foot ulceration among patients attending primary health care services

Diabetes mellitus is a global health problem with rising prevalence worldwide. Diabetes mellitus is a multi-system disease affecting many systems and tissues. Foot problems, including foot ulcerations, are common with diabetes. Foot ulceration risk factors are based on many factors and may differ from community to community. The objective of the study was to determine diabetic foot ulceration risk factors among Saudi patients with Type 2 diabetes in primary care centers. We designed a cross-sectional study and randomly selected 400 patients. Of the 400, 350 participated and completed a standard assessment form. Of the 350 subjects who participated, 57% were male and 43% were female. The prevalence of peripheral vascular disease was 15%, hallux valgus was 22.5%, inappropriate foot wear was 41%, and peripheral neuropathy was 47.5%. Peripheral neuropathy and inappropriate foot wear were the most common risk factors for foot ulceration

Synthesis, Cytotoxic Evaluation, and Structure-Activity Relationship of Substituted Quinazolinones as Cyclin-Dependent Kinase 9 Inhibitors

Cyclin-dependent kinase 9 (CDK9) plays a critical role in transcriptional elongation, through which short-lived antiapoptotic proteins are overexpressed and make cancer cells resistant to apoptosis. Therefore, CDK9 inhibition depletes antiapoptotic proteins, which in turn leads to the reinstatement of apoptosis in cancer cells. Twenty-seven compounds were synthesized, and their CDK9 inhibitory and cytotoxic activities were evaluated. Compounds 7, 9, and 25 were the most potent CDK9 inhibitors, with IC50 values of 0.115, 0.131, and 0.142 μM, respectively. The binding modes of these molecules were studied via molecular docking, which shows that they occupy the adenosine triphosphate binding site of CDK9. Of these three molecules, compound 25 shows good drug-like properties, as it does not violate Lipinski’s rule of five. In addition, this molecule shows promising ligand and lipophilic efficiency values and is an ideal candidate for further optimization