Deep Multi-Model Fusion for Human Activity Recognition Using Evolutionary Algorithms

Machine recognition of the human activities is an active research area in computer vision. In previous study, either one or two types of modalities have been used to handle this task. However, the grouping of maximum information improves the recognition accuracy of human activities. Therefore, this paper proposes an automatic human activity recognition system through deep fusion of multi-streams along with decision-level score optimization using evolutionary algorithms on RGB, depth maps and 3d skeleton joint information. Our proposed approach works in three phases, 1) space-time activity learning using two 3D Convolutional Neural Network (3DCNN) and a Long Sort Term Memory (LSTM) network from RGB, Depth and skeleton joint positions 2) Training of SVM using the activities learned from previous phase for each model and score generation using trained SVM 3) Score fusion and optimization using two Evolutionary algorithm such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The proposed approach is validated on two 3D challenging datasets, MSRDailyActivity3D and UTKinectAction3D. Experiments on these two datasets achieved 85.94% and 96.5% accuracies, respectively. The experimental results show the usefulness of the proposed representation. Furthermore, the fusion of different modalities improves recognition accuracies rather than using one or two types of information and obtains the state-of-art results

Tribological behavior of the boric acid and titanium dioxide based nanofluid in machining of EN24 steel

Turning operation is a widely recognized metal removal process in the industry. If the machining were not run efficiently, it may affect the performance of the tool and the work piece by generating higher cutting forces and the temperature as in hard steel. To minimize these effects, lubrication has to be effective in reducing these forces and lowering the tool temperature. In the present study, machining experiments were conducted on EN24 steel with the application of nano sized boric acid (50 nm) as the solid lubricant that is mixed with titanium dioxide (100 µm) in SAE 40 oil. Turning tests are conducted using tungsten carbide tool inserts under dry, wet and MQL conditions to measure and compare the cutting forces, tool temperatures and roughness of the work piece. Results indicate that boric acid enables significant reduction in the cutting forces which in combination with the titanium dioxide helps to improve the heat dissipation; an advantage that makes such lubricants an effective cutting fluid. H3BO3 and TiO2 based nanofluid resulted in reducing the surface roughness of up to 2.7 µm that is a re-duction by ~15%

Ragi Traditional But Nutritional Especially in the Era of COVID-19

Finger millet is the name commonly used to denote the crop Eleusine coracana. It is known as Ragi in many parts of India, which is an important member of the family of cereals. In fact, it is superior to many cereals like wheat and rice in terms of its micronutrient content and bioavailability. Several indigenous processing techniques may be applied to finger millets allowing it to be processed into various value-added products, which may be better in appearance, taste, flavor and acceptability. Development of value-added products that contain Ragi as one of their major components can be beneficial for food and nutrition security of Indians. Ragi may contribute to solving the issue of micronutrient deficiency and nutrition security as it is an important source of micronutrients and can be easily incorporated in various recipes and value-added products. It can therefore be a part of various nutritional programs to enhance the nutritional density of foods

X-ray structure and activity analysis of 3-bromomethyl -2-chloro-quinoline

The structure of 3-bromomethyl-2-chloro-quinoline crystallizes in the triclinic crystal space group P‘1 with unit cell parameters a=6.587(2), b=7.278(3), c=10.442(3) Å, a= 83.59(3)°, b= 75.42(2)°, g= 77.39(3)°, Z= 2, V= 471.9(3)Å3. The structure has converged to a final R-value of 0.0734. The phenyl Ring-B has normal geometry while the pyridine Ring-A has slightly distorted conformation. The asymmetry parameter calculations, i.e., DC2 and DCs for the pyridine ring indicates that the structure is planar. There exists one intramolecular hydrogen bonded interaction of the type C-H…Cl and one C-H…N intermolecular interaction. The structure is stabilized by Van der Waals forces

Two-Stage Human Activity Recognition Using 2D-ConvNet

There is huge requirement of continuous intelligent monitoring system for human activity recognition in various domains like public places, automated teller machines or healthcare sector. Increasing demand of automatic recognition of human activity in these sectors and need to reduce the cost involved in manual surveillance have motivated the research community towards deep learning techniques so that a smart monitoring system for recognition of human activities can be designed and developed. Because of low cost, high resolution and ease of availability of surveillance cameras, the authors developed a new two-stage intelligent framework for detection and recognition of human activity types inside the premises. This paper, introduces a novel framework to recognize single-limb and multi-limb human activities using a Convolution Neural Network. In the first phase single-limb and multi-limb activities are separated. Next, these separated single and multi-limb activities have been recognized using sequence-classification. For training and validation of our framework we have used the UTKinect-Action Dataset having 199 actions sequences performed by 10 users. We have achieved an overall accuracy of 97.88% in real-time recognition of the activity sequences

Deep Learning Approach to Recognize COVID-19, SARS and Streptococcus Diseases from Chest X-ray Images

51-59Corona virus disease (COVID-19) became pandemic for the world in the year 2020 and large numbers of people are infected worldwide due to the rapid widespread of this infectious virus. Pathological laboratory testing of a large number of suspects becomes challenging and producing false-negative results. Therefore, this paper aims to develop a deep learning basedapproach for automatic detection of COVID-19 infection using medical X-ray images. The proposed approach is used for the fast detection of COVID-19 along with other similar diseases such as Streptococcus, and severe acute respiratory syndrome (SARS) positive cases. A 2D-convolution neural network (2D-CNN) is used to recognize the graphical features of X-ray image’s dataset of COVID-19 positive, Streptococcus and SARSpatients. The proposed approach is tested on the COVID-chest X-Ray dataset. Experiments produced individual accuraciesof COVID-19, Streptococcus, SARS disease and normal persons are 100%, 90.9%, 91.3%, and 94.7% respectively and achieved an overall accuracy of 95.73%. From the experimental results, it is proved that the performance of the proposed approach is better as compared to the mentioned state-of-art methods

Odnos između mikroskopski izmjerenog i na osnovi volumena izračunatog polumjera eritrocita u bivola.

In the present study the relationship between microscopically measured radius, Rm, and radius calculated from the volume (assuming that the cell is spherical), Rc, is described. Mean Rc value was greater than the Rm value. Application of chi-square test showed that there is no variation between these two. The t-statistic was higher than the tabulated value. The correlation coefficient between Rm and Rc was 0. 7057. The regression equations for Rc upon Rm and Rm upon Rc was Rc = 0. 849Rm+0. 594 and Rm = 0. 5866Rc+0. 79865, respectively.Opisan je odnos između mikroskopski izmjerenog polumjera eritrocita (Rm) i polumjera izračunatog na osnovi volumena (Rc) pod pretpostavkom da su stanice okrugle. Srednja vrijednost Rc bila je veća od Rm srednje vrijednosti. Pomoću hi-kvadrat testa nisu ustanovljene varijacije u spomenutim značajkama. Statistički je t-vrijednost bila veća od izračunatih vrijednosti. Korelacijski koeficient između Rm i Rc iznosio je 0,7057. Regresijske jednadžbe za Rc prema Rm i Rm prema Rc iznosile su Rc = 0,849 Rm + 0,594 i Rm = 0,5866 Rc + 0,79865

Developing Standard Treatment Workflows—way to universal healthcare in India

Primary healthcare caters to nearly 70% of the population in India and provides treatment for approximately 80–90% of common conditions. To achieve universal health coverage (UHC), the Indian healthcare system is gearing up by initiating several schemes such as National Health Protection Scheme, Ayushman Bharat, Nutrition Supplementation Schemes, and Inderdhanush Schemes. The healthcare delivery system is facing challenges such as irrational use of medicines, over- and under-diagnosis, high out-of-pocket expenditure, lack of targeted attention to preventive and promotive health services, and poor referral mechanisms. Healthcare providers are unable to keep pace with the volume of growing new scientific evidence and rising healthcare costs as the literature is not published at the same pace. In addition, there is a lack of common standard treatment guidelines, workflows, and reference manuals from the Government of India. Indian Council of Medical Research in collaboration with the National Health Authority, Govt. of India, and the WHO India country office has developed Standard Treatment Workflows (STWs) with the objective to be utilized at various levels of healthcare starting from primary to tertiary level care. A systematic approach was adopted to formulate the STWs. An advisory committee was constituted for planning and oversight of the process. Specialty experts' group for each specialty comprised of clinicians working at government and private medical colleges and hospitals. The expert groups prioritized the topics through extensive literature searches and meeting with different stakeholders. Then, the contents of each STW were finalized in the form of single-pager infographics. These STWs were further reviewed by an editorial committee before publication. Presently, 125 STWs pertaining to 23 specialties have been developed. It needs to be ensured that STWs are implemented effectively at all levels and ensure quality healthcare at an affordable cost as part of UHC