Detection of Fake News Using Machine Learning

For some past recent years, largely since people started obtaining quick access to social media, fake news have became a serious downside and are spreading a lot of and quicker than the true news. As incontestable by the widespread effects of the big onset of fake news, humans are incapable of detecting whether the news is genuine or fake. With this, efforts have been made to research the method of fake news detection. The most popular and well-liked of such efforts is “blacklists” of sources and authors that don't seem to be trustworthy. Whereas these tools area helpful, so as to form a more complete end to end resolution, we also account for tougher cases wherever reliable sources and authors unharnessed false news. The motive of this project is to form a tool for investigation the language patterns that characterize wrong and right news through machine learning. The results of this project represent the flexibility for machine learning to be helpful during this task. We have made a model that detects several instinctive indicator of right and wrong news

Detection of Fake News Using Machine Learning

For some past recent years, largely since people started obtaining quick access to social media, fake news have became a serious downside and are spreading a lot of and quicker than the true news. As incontestable by the widespread effects of the big onset of fake news, humans are incapable of detecting whether the news is genuine or fake. With this, efforts have been made to research the method of fake news detection. The most popular and well-liked of such efforts is “blacklists” of sources and authors that don't seem to be trustworthy. Whereas these tools area helpful, so as to form a more complete end to end resolution, we also account for tougher cases wherever reliable sources and authors unharnessed false news. The motive of this project is to form a tool for investigation the language patterns that characterize wrong and right news through machine learning. The results of this project represent the flexibility for machine learning to be helpful during this task. We have made a model that detects several instinctive indicator of right and wrong news

Design of an optimal multi-layer neural network for eigenfaces based face recognition

Face recognition is one of the most popular problems in the field of image analysis. In this paper, we discuss the design of an optimal multi-layer neural network for the task of face recognition. There are many issues while designing the neural network like number of nodes in input layer, output layer and hidden layer(s), setting the values of learning rate and momentum, updating of weights. Lastly, the criteria for evaluating the performance of the neural network and stopping the learning are to be decided. We discuss all these design issues in the light of the eigenfaces based face recognition. We report the effects of variations of these parameters on number of training cycles required to get optimal results. We also list the optimized values for these parameters. In our experiments, we use two face databases namely ORL and UMIST. These databases are used to construct the eigenfaces. The original faces are reconstructed using the top eigenfaces. The factors used in the reconstruction of the faces are used as the inputs to the neural network

Lossless gray image compression using logic minimization

A novel approach for the lossless compression of gray images is presented. A prediction process is performed followed by the mapping of prediction residuals. The prediction residuals are then split into bit–planes. Two-dimensional (2D) differencing operation is applied to bit-planes prior to segmentation and classification. Performing an Exclusive-OR logic operation between neighboring pixels in the bit planes creates the difference image. The difference image can be coded more efficiently than the original image whenever the average run length of black pixels in the original image is greater than two. The 2d difference bit-plane is divided in to windows or block of size 16*16 pixels. The segmented 2d difference image is partitioned in to non-overlapping rectangular regions of all white and mixed 16*16 blocks. Each partitioned block is transformed in to Boolean switching function in cubical form, treating the pixel values as a output of the function. Minimizing these switching functions using Quine- McCluskey minimization algorithm performs compression

Dense plasma irradiated platinum with improved spin Hall effect

The impurity incorporation in host high-spin orbit coupling materials like platinum has shown improved charge-to-spin conversion by modifying the up-spin and down-spin electron trajectories by bending or skewing them in opposite directions. This enables efficient generation, manipulation, and transport of spin currents. In this study, we irradiate the platinum with non-focus dense plasma to incorporate the oxygen ion species. We systematically analyze the spin Hall angle of the oxygen plasma irradiated Pt films using spin torque ferromagnetic resonance. Our results demonstrate a 2.4 times enhancement in the spin Hall effect after plasma treatment of Pt as compared to pristine Pt. This improvement is attributed to the introduction of disorder and defects in the Pt lattice, which enhances the spin-orbit coupling and leads to more efficient charge-to-spin conversion without breaking the spin-orbit torque symmetries. Our findings offer a new method of dense plasma-based modification of material for the development of advanced spintronic devices based on Pt and other heavy metals

Standardization and development of Pasteurella multocida inactivated adjuvanted vaccine against septic pasteurellosis in pigs

315-323In piggery, septic pasteurellosis caused by Pasteurella multocida (B:2) is an issue of concern, which needs an effective vaccine. Here, we prepared a double emulsified (DE) vaccine containing 2.5 mg inactivated antigenic mass of pig field strain (B:2) (named as soron) isolated from an outbreak of septicaemic death in pigs and P. multocida P52 cattle strain (B:2) and studied their efficiency in terms of immunity to direct challenge, duration of immunity and the role of humoral and cell-mediated immunity. Both of these strains showed presence of hgbB, pfhA, nanH, ptfA, and tbPA virulence genes. The sequence analysis of bands of 760 bp product using capsular primers were obtained for soron and P52 revealed 99.2% homology between these two strains, indicating differences at genetic level. nanH and pfhA genes of soron shared 99.2% and 92.7% homology with P52, respectively suggesting differences between these two strains at genetic level. SDS-PAGE analysis of cell wall of both strains showed presence of about 15 major protein bands whereas Western blot analysis with 21 day soron immunized pig serum showed 16, 33, 47, 63 and 83 kDa polypeptides in both strains. The duration of immune responses were monitored at 3, 6 and 9 months post immunization in pigs. By direct challenge, pigs showed that the vaccines were protective at 21 days and up to 270th day post immunization. Vaccines induced a serum ELISA IgG response that peaked on 60 DPI which declined gradually up to 270th DPI in both vaccines. Stimulation index measured by lymphocyte proliferation test (LTT) indicated that the vaccine, induced cell-mediated immune response and in general percent stimulation index (SI) was higher in pigs immunized with soron vaccine at 15 days post challenge infection. The results showed that pig strain (soron) would be a potential homologous strain of P. multocida for the vaccine against pasteurellosis in place of use of cattle P. multocida P52 strain

Protective Efficiency of Pasteurella multocida A:1 Bacteriophage lysate Vaccine against Homologous and Heterologous Challenge in Poultry

Fowl cholera (FC) caused by serotypes of Pasteurella multocida includes A:1, A:3, A:4 is a highly fatal septicemic disease. Preliminary trials of P. multocida A:1 bacteriophage lysate vaccine against FC was evaluated. Lytic phage and P. multocida ratio was standardized to obtain stable lysate batches. Consequently, three batches of lytic phage preparation were produced; estimation of protein and carbohydrate content amongst batches did not shown any significant variation indicating same batches can be produced by standardized procedure. Protective response trials in poultry with P. multocida A:1, A:3, A:4 against plain lysate and alum adsorbed lysate (1% alum) on vaccinated group showed both homologous and heterologous protection compared with inactivated whole cell group provided only homologous protection. Assessment of antibody response towards P. multocida A:1, A:3, A:4 antigen evaluated by Indirect Haemagglutination test (IHA) reveals presence of protective antibody titer

Role of vitamin D supplementation in dental implant osseointegration

Successful osseointegration is one of the key criteria for a prosperous dental implant therapy which is achieved by a functional ankylosis. Vitamin D regulates the bone metabolism and bone mineralization by activating osteoclasts and osteoblasts. The effect of vitamin D coated implants on the osseointegration remains controversial. Only slight evidence supports the hypothesis that humans similarly benefit from vitamin D supplementation in terms of osseointegration. The supplementation of vitamin D appears to improve the osseointegration in animals with systemic diseases, such as vitamin D deficiency, diabetes mellitus, osteoporosis, and CKD. Slight evidence supports the hypothesis that humans similarly benefit from vitamin D supplementation in terms of osseointegration