Performance Evaluation of Face Recognition Algorithms

Biometric - based techniques have emerged for recognizing individuals instead of using passwords, PINs, smart cards, plastic cards, tokens etc fo r authenticating people . Automated face recognition has become a major field of interest. In this field several facial recognition algorithms have been explored in the past few decades . A face recognition system is expected to identify faces present in images and videos automatically. The input to the facial recognition system is a two dimensional image, while the system distinguishes the input image as a users face from a pre - determined library of faces. Finally, the output is a discerned face image. This paper deals wi th the comparison of two popular dimensionality reduction algorithms such as PCA and LDA. Here, our main goal is to evaluate the performance of Principal Component Analysis and Linear Discriminant Analysis for large training data set. Finally, we concluded that LDA outperforms PCA for the large samples of training set

Revelation of Significant Fake Rhetorical in Wrapping Bygone Utilizing Significant Learning Procedures

The developing computation control has made the profound learning calculations so powerful that making an unclear human synthesized video famously called a profound fake has got to be exceptionally straightforward. Scenarios where these practical confront swapped profound fakes are utilized to form political trouble, fake psychological warfare occasions, vindicate porn, and shakedown people groups are effortlessly imagined. In this work, we depict a modern profound learning-based strategy that can viably recognize AI-generated fake recordings from genuine videos. Our strategy can naturally be recognizing the substitution and reenactment of deep fakes. We are attempting to utilize Manufactured Intelligence (AI) to battle Fake Intelligence(AI). Our framework uses a res-next neural convolution system to extract frame-level highlights and promote the use of these highlights to prepare the long-term memory (LSTM)-based repetitive neural network (RNN) to classify whether the video is subject to art. control or not , i.e whether the video is profoundly fake or genuine. To imitate the genuine time scenarios and make the show perform way better on genuine time information, we assess our strategy on an expansive sum of adjusted and blended data-set arranged by blending the different accessible data-set like Face-Forensic, Deep Fake location challenge, and Celeb-DF. We moreover focus on  how our framework can accomplish competitive results utilizing exceptionally straightforward and strong approaches

Naïve Bayesian Classification Based Glioma Brain Tumor Segmentation Using Grey Level Co-occurrence Matrix Method

Brain tumors vary widely in size and form, making detection and diagnosis difficult. This study's main aim is to identify abnormal brain images., classify them from normal brain images, and then segment the tumor areas from the categorised brain images. In this study, we offer a technique based on the Nave Bayesian classification approach that can efficiently identify and segment brain tumors. Noises are identified and filtered out during the preprocessing phase of tumor identification. After preprocessing the brain image, GLCM and probabilistic properties are extracted. Naive Bayesian classifier is then used to train and label the retrieved features. When the tumors in a brain picture have been categorised, the watershed segmentation approach is used to isolate the tumors. This paper's brain pictures are from the BRATS 2015 data collection. The suggested approach has a classification rate of 99.2% for MR pictures of normal brain tissue and a rate of 97.3% for MR images of aberrant Glioma brain tissue. In this study, we provide a strategy for detecting and segmenting tumors that has a 97.54% Probability of Detection (POD), a 92.18% Probability of False Detection (POFD), a 98.17% Critical Success Index (CSI), and a 98.55% Percentage of Corrects (PC). The recommended Glioma brain tumour detection technique outperforms existing state-of-the-art approaches in POD, POFD, CSI, and PC because it can identify tumour locations in abnormal brain images

Synthesis, Crystal structure, DFT calculations and antimicrobial activity of 4-(4-fluoro-phenyl)-2,6-dimethyl-1,4-dihydro-pyridine-3,5-dicarboxylic acid diethyl ester

The title compound was synthesized and confirmed by FT-IR, 1H, 13C NMR analysis. The molecular structure of the compound was precisely determined by Single Crystal X-ray Diffraction (SC-XRD) analysis. The crystalized compound shows P21/C & monoclinic crystal system with cell parameters a = 9.7768 (5), b = 7.4005(3) and c = 24.8099 (12), β=93.734(2)°.The structural and electronic properties of the compound were carried out by Density Functional Theory (DFT) calculations. The compound exhibited H-bonding between N1-H1A-O1 with bond distance 2.98(7) A°).The energy gap Egap 4.53eV and Egap= 4.34eV for crystal and DFT method respectively. The molecular orbitals energies were studied through Highest Unoccupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) analysis. The softness and hardness of the molecule was studied through Global Chemical Reactivity Descriptors (GCRD). The electrophilic and nucleophilic characters were studied through Molecular Electrostatic Potential (MEP) studies. The antimicrobial studies were carried out by in-vitro method against 6 microorganisms

Synthesis, Characterization, Crystal Structure of 4-(4-Bromo-phenyl)-2,6-dimethyl-1,4-dihydro-pyridine-3,5-dicarboxylic Acid Diethyl Ester: Hirshfeld Surface Analysis and DFT Calculations

The title compound dihydro-pyridine was synthesized and structure is established by FT-IR, 1H NMR, and 13C NMR spectral analysis. The SC-XRD analysis has been carried out for the determination of molecular structure and its disclosed that crystal relates to monoclinic crystal phase, P21/n1 space group and cell parameters are a= 10.2314(2), b = 7.5215(1), c = 24.5475(4), α = 90, β = 97.921(1)° γ = 90 with 0.34 x 0.33 x 0.30 crystal size. The crystal lattice exhibits inter-molecular H-bonding between N1—H1A—O1. Further inter contacts of the crystal lattice were determined by 3-D Hirshfeld surface (HSA) as well as percentage of contributions have been computed through 2D finger plot depiction. Moreover, bond length, bond angle and torsion angles have been correlated to respective output results of B3LYP/6-311++G(d,p). The electrophilic and nucleophilic characters have been studied through molecular electrostatic potential (MEP) analysis. © 2020 NIODC. All rights reserved ©2022 National Information and Documentation Center (NIDOC).We record our sincere thanks to the (CIMF), Periyar University, Salem for providing the facilities for single crystal XRD analysis and SAIF-VIT for providing 1H, 13C NMR analysis. The authors also thanks to Jamal instrumentation facility (JIF), Post Graduate and Research Department of Chemistry, Jamal Mohamed College, Tiruchirappalli for providing necessary lab facilities

Bio-nanotechnology application in wastewater treatment

The nanoparticles have received high interest in the field of medicine and water purification, however, the nanomaterials produced by chemical and physical methods are considered hazardous, expensive, and leave behind harmful substances to the environment. This chapter aimed to focus on green-synthesized nanoparticles and their medical applications. Moreover, the chapter highlighted the applicability of the metallic nanoparticles (MNPs) in the inactivation of microbial cells due to their high surface and small particle size. Modifying nanomaterials produced by green-methods is safe, inexpensive, and easy. Therefore, the control and modification of nanoparticles and their properties were also discussed

ESolvent-free, enzyme-catalyzed biodiesel production from mango, neem, and shea oils via response surface methodology

Mango, neem and shea kernels produce non-conventional oils whose potentials are not fully exploited. To give an added value to these oils, they were transesterified into biodiesel in a solvent-free system using immobilized enzyme lipozyme from Mucor miehei. The Doehlert experimental design was used to evaluate the methyl ester (ME) yields as influenced by enzyme concentration—EC, temperature—T, added water content—AWC, and reaction time—RT. Biodiesel yields were quantified by (1)H NMR spectroscopy and subsequently modeled by a second order polynomial equation with interactions. Lipozyme enzymes were more tolerant to high temperatures in neem and shea oils reaction media compared to that of mango oil. The optimum reaction conditions EC, T, AWC, and RT assuring near complete conversion were as follows: mango oil 7.25 %, 36.6 °C, 10.9 %, 36.4 h; neem oil EC = 7.19 %, T = 45.7 °C, AWC = 8.43 %, RT = 25.08 h; and shea oil EC = 4.43 %, T = 45.65 °C, AWC = 6.21 % and RT = 25.08 h. Validation experiments of these optimum conditions gave ME yields of 98.1 ± 1.0, 98.5 ± 1.6 and 99.3 ± 0.4 % for mango, neem and shea oils, respectively, which all met ASTM biodiesel standards

Ultrasonic intensification as a tool for enhanced microbial biofuel yields

peer-reviewedUltrasonication has recently received attention as a novel bioprocessing tool for process intensification in many areas of downstream processing. Ultrasonic intensification (periodic ultrasonic treatment during the fermentation process) can result in a more effective homogenization of biomass and faster energy and mass transfer to biomass over short time periods which can result in enhanced microbial growth. Ultrasonic intensification can allow the rapid selective extraction of specific biomass components and can enhance product yields which can be of economic benefit. This review focuses on the role of ultrasonication in the extraction and yield enhancement of compounds from various microbial sources, specifically algal and cyanobacterial biomass with a focus on the production of biofuels. The operating principles associated with the process of ultrasonication and the influence of various operating conditions including ultrasonic frequency, power intensity, ultrasonic duration, reactor designs and kinetics applied for ultrasonic intensification are also described

Anaerobic digestion and gasification of seaweed

The potential of algal biomass as a source of liquid and gaseous biofuels is a highly topical theme, with over 70 years of sometimes intensive research and considerable financial investment. A wide range of unit operations can be combined to produce algal biofuel, but as yet there is no successful commercial system producing such biofuel. This suggests that there are major technical and engineering difficulties to be resolved before economically viable algal biofuel production can be achieved. Both gasification and anaerobic digestion have been suggested as promising methods for exploiting bioenergy from biomass, and two major projects have been funded in the UK on the gasification and anaerobic digestion of seaweed, MacroBioCrude and SeaGas. This chapter discusses the use of gasification and anaerobic digestion of seaweed for the production of biofuel