Automatic Attendance Monitoring System

The attendance is taken in every organization. Traditional approach for attendance is, professor calls student name & record attendance. For each lecture this is wastage of time. To avoid these losses, we are about to use automatic process which is based on image processing. In this project approach, we are using face detection & face recognition system. The first phase is pre-processing where the face detection is processed through the step image processing. It includes the face detection and face recognition process. Second phase is feature extraction. Step by step execution of these techniques (Image Processing) helps to achieve the final output. The working of this project is to detect and recognize the face and mark the attendance for the corresponding face in the database. Input of this project is face detection and recognition and output is to mark the attendance. Our project is being presented as a solution for the Automatic Attendance Marking System. It is designed to be reliable and low power. The Automatic face detection and recognition proposed to attendance marking in database acts as the solution for the automatic attendance marking system.

A Review on Human Gait Detection

The human gait is the identification of human locomotive based on limbs position or action The tracking of human gait can help in various applications like normal and abnormal gait fall detection gender detection age detection biometrics and in some terrorist and criminal activity detection The present work carried out is a review of various methodologies employed in human gait detection The analysis describes that the different feature extraction and machine learning techniques to be adopted for the identification of human gait based on the purpose of the applicatio

A secured and optimized deep recurrent neural network (DRNN) scheme for remote health monitoring system with edge computing

Patients now want a contemporary, advanced healthcare system that is faster and more individualized and that can keep up with their changing needs. An edge computing environment, in conjunction with 5G speeds and contemporary computing techniques, is the solution for the latency and energy efficiency criteria to be satisfied for a real-time collection and analysis of health data. The feature of optimum computing approaches, including encryption, authentication, and classification that are employed on the devices deployed in an edge-computing architecture, has been ignored by previous healthcare systems, which have concentrated on novel fog architecture and sensor kinds. To avoid this problem in this paper, an Optimized Deep Recurrent Neural Network (O-DRNN) model is used with a multitier secured architecture. Initially, the data obtained from the patient are sent to the healthcare server in edge computing and the processed data are stored in the cloud using the Elliptic Curve Key Agreement Scheme (ECKAS) security model. The data is pre-processed and optimal features are selected using the Particle Swarm Optimization (PSO) algorithm. O-DRNN algorithm hyper-parameters are optimized using Bayesian optimization for better diagnosis. The proposed work offers superior outcomes in terms of accuracy and encryption latency while using computational cloud services

Juvenile granulosa cell tumour: a rare clinical entity

Ovarian cancer is the third most common neoplasm of the female genital tract. Based on the cell type of origin, primary ovarian malignancies are classified into surface epithelium, germ cell, and sex cord tumors. Sex cord tumors account for 1% to 2% of ovarian malignancies. They may contain granulosa cells, theca cells, sertoli cells, or fibroblasts of gonadal stromal origin. Granulosa Cell Tumours (GCTs) account for approximately 2-5% of all ovarian tumors and can be divided into adult (95%) and juvenile (5%) types based on histologic findings. GCTs secrete estrogen thus resulting in menstrual irregularities in the affected individual. More serious estrogen effects can occur in various end organs such as uterus resulting in endometrial hyperplasia, endometrial adenocarcinomas and increased risk of breast cancers. Androgen production is also reported but rare and produces virilization in the affected women. Juvenile Granulosa Cell Tumours (JGCTs) are clinically & histopathologically distinct from the GCTs. They are rarely encountered but mostly in youngsters. Surgery is the primary modality of treatment with chemotherapy being reserved for advanced or recurrent disease states. We herewith report an interesting case of JGCT in a young teenage girl.

Determination of gestational age: correlation between foetal biometry and transverse cerebellar diameter in women with uncomplicated pregnancy

Background: Transverse Cerebellar Diameter (TCD) serves as a reliable predictor of gestational age in foetus and is a standard against which aberrations in other foetal parameters can be compared, especially when the GA cannot be determined by the date of last menstrual period or early pregnancy scan, TCD is one foetal parameter that has remained consistently superior in predicting gestational age in both singleton and twin gestation. Aim of the study was to assess and evaluate the effectiveness of transverse cerebellar diameter by using ultrasonography for determining the gestational age of the foetus.Methods: A cross-sectional study was done in 100 uncomplicated pregnant patients between the 15th week of gestation to term referred from routine antenatal clinic in outpatient and in-patient department of Obstetrics and Gynecology department of Vinayaka Mission Krupananda Variyar medical college and hospital, Salem during study period April 2015-March 2016. TCD is obtained in the axial plane in the cerebellar view i.e. with a slight rotation of the transducer approximately 30° from the conventional thalamic plane where the biparietal diameter is measured using the cavum septi pellucidi, third ventricle and thalami as landmarks.Results: The correlation of transcerebellar diameter (TCD) with that of BPD (bi-parietal diameter) had shown a perfect positive correlation (r = 0.978) and a similar type of correlation was also seen with HC (head circumference) (r = 0.979), AC (abdominal circumference) (r = 0.966), FL (femur length) (r = 0.976) and USG GA (ultrasonogram gestational age) (r = 0.983).Conclusions: In the normally developing foetus, the TCD increases in a linear fashion with advancing gestational age. The data of this study suggest foetal TCD on ultrasound is a reliable predictive biometric parameter of gestational age

An Ensemble Learning Approach for Fast Disaster Response using Social Media Analytics

Natural disaster happens, as a result of natural hazards that cause financial, environmental or human losses. Natural disasters strike unexpectedly, affecting the lives of tens of thousands of people. During the flood, social media sites were also heavily used to disseminate information about flooded areas, rescue agencies, food and relief centres. This work proposes an ensemble learning strategy for combining and analysing social media data in order to close the gap and progress in catastrophic situation. To enable scalability and broad accessibility of the dynamic streaming of multimodal data namely text, image, audio and video, this work is designed around social media data. A fusion technique was employed at the decision level, based on a database of 15 characteristics for more than 300 disasters around the world (Trained with MNIST dataset 60000 training images and 10000 testing images).  This work allows the collected multimodal social media data to share a common semantic space, making individual variable prediction easier. Each  merged numerical vector(tensors) of text and audio  is sent into the K-CNN algorithm, which is an  unsupervised learning algorithm (K-CNN), and the  image and video data is given to a deep learning  based Progressive Neural Artificial Search (PNAS).  The trained data acts as a predictor for future  incidents, allowing for the estimation of total  deaths, total individuals impacted, and total  damage, as well as specific suggestions for food,  shelter and housing inspections. To make such a prediction, the trained model is presented a satellite image from before the accident as well as the geographic and demographic conditions, which is expected to result in a prediction accuracy of more than 85%

Expression of a malarial Hsp70 improves defects in chaperone-dependent activities in ssa1 mutant yeast

Plasmodium falciparum causes the most virulent form of malaria and encodes a large number of molecular chaperones. Because the parasite encounters radically different environments during its lifecycle, many members of this chaperone ensemble may be essential for P. falciparum survival. Therefore, Plasmodium chaperones represent novel therapeutic targets, but to establish the mechanism of action of any developed therapeutics, it is critical to ascertain the functions of these chaperones. To this end, we report the development of a yeast expression system for PfHsp70-1, a P. falciparum cytoplasmic chaperone. We found that PfHsp70-1 repairs mutant growth phenotypes in yeast strains lacking the two primary cytosolic Hsp70s, SSA1 and SSA2, and in strains harboring a temperature sensitive SSA1 allele. PfHsp70-1 also supported chaperone-dependent processes such as protein translocation and ER associated degradation, and ameliorated the toxic effects of oxidative stress. By introducing engineered forms of PfHsp70-1 into the mutant strains, we discovered that rescue requires PfHsp70-1 ATPase activity. Together, we conclude that yeast can be co-opted to rapidly uncover specific cellular activities mediated by malarial chaperones. © 2011 Bell et al