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

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

PRIMARY CENTRAL NERVOUS SYSTEM EFFUSION PLASMABLASTIC LYMPHOMA IN IMMUNOCOMPROMISED PATIENT: A RARE PHENOMENON

Primary effusion lymphoma (PEL) is an aggressive neoplasm with a high rate of fatality. PEL cells are known to have morphological diversities, which range from immunoblastic or plasmablastic to anaplastic. Most of these cases are described in immunocompromised as well as immunocompetent patients. Plasmablastic lymphoma remains a diagnostic challenge, especially when encountered with the presentation as PEL. In spite of therapeutic advances, PEL remains an aggressive disease with a high rate of fatality. We describe one case of this extremely rare neoplasm in an immunocompromised patient presenting in the form of primary central nervous system effusion plasmablastic lymphoma. To the best of our knowledge, this is the first case ever been reported in the literature

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

A Remotely Operable Facility for Fabrication of Fuel Pins for test Irradiation

AbstractA laboratory scale facility has been set up for fabrication of test fuel pins through sol-gel route for irradiation in FBTR, Kalpakkam. The facility is a train of glove boxes fitted with master slave manipulators for carrying out various operations involved in the fuel fabricat ion process. The paper describes the design features of the equipment and mechanisms for automation, developed for microsphere production and other processes. The design features include control system and vision systems for man- machine interface

Epidermoid cyst in inguinal canal: A rare presentation

The patients presenting with lump in inguinal area are mostly suspected as hernias. Epidermoid cyst commonly presenting in head and neck region rarely may develop from inguinal canal structures. We present here a rare case of epidermoid cyst measuring 7×8 cm as a content of inguinal canal diagnosed by ultrasonography. Surgical excision was done and confirmed as epidermoid cyst by histopathology. We conclude that cutaneous cysts in inguinal area may be a presentation and should be kept in mind for differential diagnosis

Situs Ambiguous Anomaly during Laparoscopic Cholecystectomy in an Adult Female

Situs anomalies are rare structural defects affecting 0.01% of general population. They present with multisystem structural defects mostly involving cardiovascular, respiratory and GI systems. Situs abnormality with presence of multiple spleen is termed as left atrial isomerism with anatomical and structural differences to its countertype situs ambiguous with asplenia (right atrial isomerism). In this case report, we present an adult case of situs ambiguous anomaly which was diagnosed incidentally during laparoscopic cholecystectomy. The patient had enlarged left lobe of liver, multiple splenules on right side, malrotated small and large gut, interrupted inferior vena cava with azygos continuation, and bilateral bilobed lungs. It is concluded that variations in situs ambiguous cases differ and a single description is not possible. It is crucial to reveal these variations by using imaging modalities and being aware of them prior to surgery and invasive intervention to prevents the possible risks and complications. &nbsp