A Face Recognition Method Using Deep Learning To Identify Mask And Unmask Objects

At the present, the use of face masks is growing day by day and it is mandated in most places across the world. People are encouraged to cover their faces when in public areas to avoid the spread of infection which can minimize the transmission of Covid-19 by 65 percent (according to the public health officials). So, it is important to detect people not wearing face masks. Additionally, face recognition has been applied to a wide area for security verification purposes since its performance, accuracy, and reliability [15] are better than any other traditional techniques like fingerprints, passwords, PINs, and so on. In recent years, facial recognition is becoming a challenging task because of various occlusions or masks like the existence of sunglasses, scarves, hats, and the use of make-up or disguise ingredients. So, the face recognition accuracy rate is affected by these types of masks. Moreover, the use of face masks has made conventional facial recognition technology ineffective in many scenarios, such as face authentication, security check, tracking school, and unlocking phones and laptops. As a result, we proposed a solution, Masked Facial Recognition (MFR) which can identify masked and unmasked people so individuals wearing a face mask do not need to take it out to authenticate themselves. We used the Deep Learning model, Inception ResNet V1 to train our model. The CASIA dataset [17] is applied for training images and the LFW (Labeled Faces in the Wild) dataset [18] with artificial marked faces are used for model evaluation purposes. The training and testing masked datasets are created using a Computer Vision-based approach (Dlib). We received an accuracy of around 96 percent for our three different trained models. As a result, the purposed work could be utilized effortlessly for both masked and unmasked face recognition and detection systems that are designed for safety and security verification purposes without any challenges

Effect of alkalinity on growth performance of Jatropha curcas inoculated with PGPR and AM fungi

A pot experiment was conducted to assess the effect of soil alkalinity on emergence, growth, leaf relative water content, total soluble sugar and soluble protein of seedlings of Jatropha curcas L. Na2CO3 was added to the soil and alkalinity was maintained at 0.1%, 0.2%, 0.3%, 0.4%, and 0.5%. In general increased alkalinity caused reduction in growth.   Hence we designed the experiment to test the efficacy of beneficial microbes (Azotobacter, Microfoss and arbuscular mycorrhizal fungi) individually and in combinations to alleviate the stressful effect of alkaline soil. The data pertaining to the effect of bioinoculants on different parameters of Jatropha curcas under alkaline stress were collected and statistically analyzed. The effect of bioinoculants on percentage seed germination and survival at 0.4% of Na2CO3 was found to be in order of; Azotobacter+AMF> AMF>Azotobacter+ Microfoss>Microfoss > Azotobacter >control (no germination) while at 0.5 % Na2CO3 germination was almost nil with all treatments. The survival percentages with respect to all treatments were found to be significant at 0.4%, Na2CO3 level over control. The combination of AM fungi and Azotobacter increased plant height, shoot diameter, shoot dry weight, leaf relative water content and soluble sugar content and decreased level of soluble protein at 0.4 % of Na2CO3 over other treatments. We conclude that the combinations of Azotobacter and AMF performed well up to 0.4 % of Na2CO3 in soil

Design , Development and Performance Evaluation of Intelligence Sensors

Many electronic devices, instruments and sensors exhibit inherent nonlinear input-output characteristics. Nonlinearity also creeps in due to change in environmental conditions such as temperature and humidity. In addition, aging of the sensors also introduce nonlinearity. Due to such nonlinearities direct digital readout is not possible. As a result the devices or sensors are used only in the linear region of their characteristics. In other words the usable range of these devices gets restricted due to nonlinearity problem. The accuracy of measurement is also affected if the full range of the instrument is used. The nonlinearity present in the characteristics is usually time-varying and unpredictable as it depends on many uncertain factors stated above. Hence the prime objective of the thesis is to study the nonlinearity problem associated with these devices and suggest novel methods of circumventing these effects by suitably designing intelligent systems. In the present investigation,..

Third generation cephalosporin-resistance in Klebsiella pneumoniae isolates: an emerging threat

Background: Newer generation cephalosporin-resistance among Klebsiella pneumoniae organisms has increased recently. Present study is undertaken to find incidence, antimicrobial susceptibility and prevalence of extended spectrum beta-lactamase (ESBL) in K. pneumoniae isolates in a tertiary care hospital.Methods: Prospective study was carried out between June to December 2011. Samples of pus, blood, urine, cerebro-spinal fluid, stool, peritoneal, pleural and synovial fluid were collected from indoor and outdoor patients for isolation and antimicrobial susceptibility pattern of K. pneumoniae in the department of microbiology, G.R. Medical College Gwalior, M.P. Ceftazidime resistant K. pneumoniae were subjected to Phenotypic Confirmatory Disc Diffusion Test (PCDDT) and Double Disc Synergy Test (DDST) for detection of ESBL.Results: Out of 2480 samples collected a total of 530 K. pneumoniae were isolated and subjected to antimicrobial susceptibility. Antibiotic sensitivity to imipenem, cefoperazone, amikacin and ofloxacin were 82, 74, 73 and 72% respectively whereas sensitivity to ceftizoxime, ceftriaxone cefotaxime, ceftazidime ranged between 47-50%. K. pneumoniae were found to be resistant to ampicillin, co-trimoxazole, doxycycline and gentamicin, by 91, 82, 54 and 50% respectively. Among third generation cephalosporins K. pneumoniae were least sensitive (47%) to ceftazidime. About 33 and 32% of the ceftazidime resistant strains were found to be ESBL positive by PCDDT and DDST respectively.Conclusions: This study has shown that prevalence of ESBL producing K. pneumoniae is the most important reason for increased resistance to third generation cephalosporins. There is need to carry out tests for detection of ESBL producing bacteria routinely

Ovarian mature cystic teratoma with malignant transformation and fistula formation into ascending colon: an unusual presentation of a dermoid cyst

Mature cystic teratoma of ovarian origin in postmenopausal female is an unusual differentiation. It is usually a straight forward diagnosis on imaging due to its fat and calcified contents. Here we present how a malignant transformation in mature cystic teratoma causing fistulous communication with bowel loops makes the diagnosis difficult even after using almost all the imaging modalities in the patient's workup and subsequently how it was managed surgically.