Mouth Image Based Person Authentication Using DWLSTM and GRU

Recently several classification methods were introduced to solve mouth based biometric authentication systems. The results of previous investigations into mouth prints are insufficient and produce lesser authentication results. This is mainly due to the difficulties that accompany any analysis of the mouths: mouths are very flexible and pliable, and successive mouth print impressions even those obtained from the same person may significantly differ from one other. The existing machine learning methods, may not achieve higher performance and only few methods are available using deep learning for mouth biometric authentication. The use of deep learning based mouth biometrics authentication gives higher results than usual machine learning methods. The proposed mouth based biometric authentication (MBBA) system is rigorously examined with real world data and challenges with the purpose that could be expected on mouth-based solution deployed on a mobile device. The proposed system has three major steps such as (1) database collection, (2) creating model for authentication, (3) performance evaluation. The database is collected from Annamalai University deep learning laboratory which consists of 5000 video frames belongs to 10 persons. The person authentication model is created using divergence weight long short term memory (DWLSTM) and gated recurrent unit (GRU) to capture the temporal relationship in mouth images of a person. The existing and proposed methods are implemented via the Anaconda with Jupyter notebook. Finally the results of the proposed model are compared against existing methods such as support vector machine (SVM), and Probabilistic Neural Network (PNN) with respect to metrics like precision, recall, F1-score, and accuracy of mouth

Nano Pesticides Application in Agriculture and Their Impact on Environment

Environmental contamination and the tolerance developed by the pests, pathogens are some of the environmental issues related to the aimless utilization of chemical pesticides. It has became matter of serious concern for environment, food quality and soil health. Nanotechnology, envisaged as a swiftly emerging field has capability to reform food systems in agriculture. Nanotechnology provides an imperishable solution to these problems by the establishment of nano-pesticides. The functional components or the conveyor molecules used are of nano size. The performance of these nano sized particles is much better the traditional pesticides, as the smaller size aids in proper spreading on the pest surface. Amelioration in solubility of operational components, betterment in stability of formulation, gradual liberation of operational components and enhancement in mobility are some of the paramount advantages of nano particles attributed to the minute size of particles and greater surface area. Thus, nano particles have strengthened activity against target pests in comparison to bulk materials. Furthermore, nano-formulations sustain productive use in agriculture by offering systemic properties, uniform leaf coverage and enhanced soil properties. Despite all the positive aspects, it might have certain negative effects as well, like exposure of humans through distinct routes Viz, exposure to nano pesticides either directly or indirectly like adsorption through skin, or inhalation while breathing air or transfer from one energy level to another by taking contaminated food and water

Real Time Face Authentication System Using Stacked Deep Auto Encoder for Facial Reconstruction

Any human being has unique biological traits biological characteristics which can be studied using Biometrics which encompasses an individuals characteristics like DNA, face, finger prints, voice, signatures etc. Human faces as an element of authentication are being increasingly used where biometrics add value in terms of quantifying an individual’s natural data. Facial authentications validate personal identities based on facial images with 1-1 matches. These kinds of authenticating applications have been applied in a variety of areas including banking applications and personal mobile devices. RTFAs (Real Time Face Authentications) based systems are a necessity for ATMs (Automated Teller Machines) in banking for enhanced security. Several machine learning methods have been introduced RTFA based systems. The overall performance of the traditional machine learning methods is lesser due to considering the same image for authentication; noises presented in the image samples. To solve this issues, in this work authentication is performed based on the reconstructed image via deep learning method. The major novelty of the work is to apply a deep learning method for image reconstruction and authentication. None of the existing methods will apply deep learning methods for reconstructing of image with real time authentication. This work proposes a novel supervised DLT (Deep Learning Technique) based on SDAE (Stacked Deep Auto Encoder) for image reconstructions in RTFA based systems. The proposed system consists of five major steps: (1) database collection, (2) Pre-processing, (3) SDAE modelling and image reconstruction, (4). RTFA based system, and (5) Performance evaluation. For the first step, 220 faces are collected in real time from 5 persons (each 44) with image size of 92*92. In the second step, facial images, RGB (Red Green Blue) images are converted to Gray scale which is resized into 32*32-pixel images. In the third step, SDAE model reconstructs the image which is then used by the RTFR system to identify a person. The reconstructed facial image is then compared with previously registered images using threshold based NCCs (Normalized Cross Correlations). The proposed SDAE model is evaluated for reconstructions of the original facial images in terms of PSNRs (Peak Signal To Noise Ratios), MSEs (Mean Square Errors), and RMSEs (Root Mean Square Errors). The proposed SADE classifier gives lesser RMSE results of 0.1000 whereas other methods such as CNN, LSTM and VGG16 gives increased MSE results of 0.3500, 0.30741 and 0.27423

Amelioration of hyperglycaemia and modulation of antioxidant status by Alcea rosea seeds in alloxan-induced diabetic rats

Context: Alcea rosea L. (Malvaceae) has various medicinal uses including anticancer, anti-inflammatory and analgesic properties. However, there is no report on its antidiabetic activity. Objective: Alcea rosea seed extracts were evaluated for antihyperglycaemic and antioxidative potential in diabetic rats. Materials and methods: Single intra-peritoneal injection of alloxan (130 mg/kg b.w.) was used for induction of diabetes in Albino Wistar rats. Antihyperglycaemic and antioxidant activities of methanol and aqueous extracts of Alcea rosea seed (100 and 300 mg/kg b.w.), administered orally on daily basis for 15 days, were assessed in vivo for fasting blood glucose level and antioxidant status of liver and pancreas. Metformin was used as a positive control. Results: Aqueous and methanol extracts (300 mg/kg b.w.) decreased blood glucose level in diabetic rats by 24% and 46%, respectively. Administration of aqueous and methanol extracts at 300 mg/kg b.w. significantly (p < 0.01) modulated the antioxidant status of liver in diabetic rats by increasing levels of GR (22.5 ± 1.0, 24.4 ± 1.02 μg GSSG utilized/min/mg of protein), GPx (20.7 ± 1.2, 23.6 ± 2.04 μg GSH utilized/min/mg of protein), SOD (36.1 ± 1.7, 39.05 ± 1.5 units/mg of protein) and CAT (1744.5 ± 132.5, 1956.6 ± 125.2 nmol H2O2 decomposed/min/mg of protein), respectively. Similar results were observed for pancreas. Discussion and conclusions: Antihyperglycaemic and antioxidative potentials of Alcea rosea seeds suggest its usefulness in management of diabetes and its complications. This is the first report on antidiabetic activity of this plant

Prospects of Sharia Governance in Islamic Finance Industry: Jurisdictions, Standards, and Implications

The paper aims to explore the establishment, organizational setup, and relevance of the International regulatory/standard setting institutions as Sharī‘ah governance platforms primarily AAOIFI (Accounting and Auditing Organization for Islamic Finance Institutions) and IFSB (Islamic Financial Services Board). The role of IIFA (International Islamic Fiqh Academy Jeddah) IFC (Islamic Fiqh Council of Muslim World League MWL) and ISRA (International Sharī‘ah Research Academy for Islamic Finance) and supportive Shari ‘ah compliance platforms for Islamic Finance Industry (IFI) in the corporate and academic dimensions. The study is qualitative analysis of related Guiding Principles, which enshrine the Sharī‘ah governance framework (SGF) in IFI. The discussion highlights that the (SGF) is the particular aspect in the Islamic Finance Industry (IFI), which embodies the legitimacy of instruments, and generates the trust of stakeholders and shareholders in Islamic finance. The paper shows that analysis will support the understanding of Sharī‘ah governance and jurisdiction of the Islamic finance industry in the contemporary Banking and finance sector. The outcome of the predicted hypothesis will enable to suggest and modify the ongoing banking practices in Islamic corporate with strict adherence to the sharīa standards

Isolation, molecular characterization and prevalence of Clostridium perfringens in sheep and goats of Kashmir Himalayas, India

Aim: The study was conducted to report the occurrence of the Clostridium perfringens in sheep and goats of the Kashmir valley for the 1st time and to characterize them molecularly with respect to toxin genes to determine the prevalence of the various toxinotypes. Materials and Methods: A total of 177 samples (152 from sheep and 25 from goats) collected from healthy, diarrheic animals, and morbid material of animals suspected to have died of enterotoxaemia were screened for C. perfringens toxinotypes. The presumptive positive isolates were confirmed using 16S rRNA gene-based polymerase chain reaction (PCR). All the confirmed isolates were screened for six toxin genes, namely; cpa, cpb, etx, cpi, cpb2, and cpe using a multiplex PCR. Results: The PCR amplification of 16S rRNA gene revealed that out of 177 samples collected, 125 (70.62%) were found positive for C. perfringens, of which 110 (72.36%) were from sheep and 15 (60%) were from goats. The highest prevalence of C. perfringens toxinotype D was observed in lambs (56.16%) and kids (46.16%) followed by 3.84% in adult sheep while it was absent in samples obtained from adult goats. The multiplex PCR revealed that 67 (60.90%) isolates from sheep and 8 (53.33%) isolates from goats belonged to toxinotype A, while 43 (39.09%) isolates from sheep and 7 (46.66%) isolates from goats were detected as toxinotype D. None of the isolates was found to be toxinotype B, C, or E. All the C. perfringens toxinotype A isolates from sheep were negative for both cpb2 and cpe genes, however, 27.90% toxinotype D isolates from sheep carried cpb2 gene, and 6.97% possessed cpe gene. In contrast, 12.50% C. perfringens toxinotype A isolates from goats harbored cpb2 and cpe genes while 14.28% isolates belonging to toxinotype D carried cpb2 and cpe genes, respectively. Conclusion: The high prevalence of C. perfringens was observed, even in day-old lambs. The toxinotypes A and D are prevalent in both sheep and goats. The severity of disease and mortality may be associated with the presence of minor toxins in both the detected toxinotypes

Phytoliths as proxies of the past

Phytoliths are silica casts of plant cells, created within and between living tissues across almost all plant clades. Because they are abundant, durable and distinctive, phytoliths are used to deduce historic vegetation patterns and human uses across the fields of archeology, paleoethnobotany, paleoecology, and historical ecology, particularly at sites where preservation of larger plant-derived samples is poor. Nonetheless, phytolith research has recently contributed to advances in biogeochemical cycling and carbon sequestration. Although much progress has been made over the past few decades, some basic methodological concerns in phytolith systematics and Si cycling still hamper the overall development of this emerging field of science. Here, we first review basic scenarios of phytolith studies across different disciplines of science and then advocate interdisciplinary phytolith research to overcome the challenges of phytolith systematics, inform the representation of Si and C cycling in biogeochemical models, and improve the utility of phytoliths as proxies in archeology and paleontology.This study has benefited from the support of the University Grants commission (UGC), India provided to Irfan Rashid under Raman fellowship Programme.Peer Reviewe