Fake news is the deliberate spread of false or misleading information through traditional and social media for political or financial gain. The impact of fake news can be significant, causing harm to individuals and organizations and undermining trust in legitimate news sources. Detecting fake news is crucial to promote a well-informed society and protect against the harmful effects. Tools such as machine learning and natural language processing are being developed to help identify fake news automatically. Necessity of fake news detection is very important to maintain a trustworthy and responsible media environment. We have used Word2Vec model for word vectorization and represents words in a multi- dimensional space based on their semantic and syntactic relationships. The use of the LSTM with 256 units allows our model to capture the sequential nature of the data and make predictions based on past information. The proposed model uses Word2Vec and LSTM models to provide a powerful approach to fake news detection, combining the ability to capture the complexity of language and the sequential nature of the data. The model has the potential to accurately detect fake news and promote a well-informed society. The accuracy achieved by building the model was 97%.
A repetitive sequence specific to Mycobacterium tuberculosis was isolated from a λ gt11 library of M. tuberculosis by DNA–DNA hybridization using genomic DNA of M. tuberculosis as probe followed by subtractive hybridization with a cocktail of other mycobacterial DNA. This led to identification of CD192, a 1291 bp fragment of M. tuberculosis containing repetitive sequences, which produced positive hybridization signals with M. tuberculosis DNA within 30 min. Nucleotide sequencing revealed the presence of several direct and inverted repeats within the 1291 bp fragment that belonged to a PPE family gene (Rv0355) of M. tuberculosis . The use of CD192 as a DNA probe for the identification of M. tuberculosis in culture and clinical samples was investigated. The 1291 bp sequence was present in M. tuberculosis , Mycobacterium bovis and M. bovis BCG, but was not present in many of the other mycobacterial strains tested, including M. tuberculosis H37Ra. More than 300 clinical isolates of M. tuberculosis were probed with CD192, and the presence of the 1291 bp sequence was observed in all the clinical strains, including those lacking IS 6110 . The sequence displayed RFLP among the clinical isolates. A PCR assay was developed which detected M. tuberculosis with 100 % specificity from specimens of sputum, cerebrospinal fluid and pleural effusion from clinically diagnosed cases of tuberculosis.
This thoughtfully organized book has been designed to provide its reader with sound foundations of data mining and data warehousing. The number of chapter, chapter topics and the contents of each chapter has been carefully chosen to introduce the reader to all important concepts through a single book. Every word and illustration has been tailored to convey to the reader that using tools can be an enjoyable and gratifying personal experience. Each chapter addresses the fundamental concepts, popular technologies and current state of the art topics.Complete with numerous illustrations and example, chapter summaries, end-of-chapter questions and a glossary of important terms.
potato stem necrosis disease (PSND) is a problem in early planted potato crop, caused by groundnut bud necrosis virus (GBNV) vectored by thrips. diagnosis of GBNV in leaf samples and thrips tissues was carried out using a simple technique i.e. print capture reverse transcriptase polymerase chain reaction (PC-RT-PCR). This technique involves spotting of samples onto NCMS, eluting viral rna from the NCM in sterile distilled water (20 µl) at 95°c for 10 min, CDNA synthesis followed by PCR amplification and analysis of the PCR product on 1.6% agarose gel. GBNV was detected in the leaf samples and thrips tissues which amplify at 800 bp in the first planted crop while, samples from later planted potato crop did not correspond to the results like the first crop.
Communication is the main motive in any Networks whether it is Wireless Sensor Network, Ad-Hoc networks, Mobile Networks, Wired Networks, Local Area Network, Metropolitan Area Network, Wireless Area Network etc, hence it must be energy efficient. The main parameters for energy efficient communication are maximizing network lifetime, saving energy at the different nodes, sending the packets in minimum time delay, higher throughput etc. This paper focuses mainly on the energy efficient communication with the help of Adjacency Matrix in the Wireless Sensor Networks. The energy efficient scheduling can be done by putting the idle node in to sleep node so energy at the idle node can be saved. The proposed model in this paper first forms the adjacency matrix and broadcasts the information about the total number of existing nodes with depths to the other nodes in the same cluster from controller node. When every node receives the node information about the other nodes for same cluster they communicate based on the shortest depths and schedules the idle node in to sleep mode for a specific time threshold so energy at the idle nodes can be saved.
This book is intended to serve as a complete guide of the Steganography. It provides an abstract, conceptual and logical view to visualize the general features of Steganography. Every word and illustration has been tailored to convey to reader that using Steganography can be an enjoyable and gratifying personal experience. This thoughtfully organized book has been designed to provide its readers with a sound foundation of Steganography Techniques. Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.Information hiding is an emerging research area, which encompasses applications such as copyright protection for digital media, watermarking, fingerprinting, and steganography.Steganography hide the secrete message within the host data set and presence imperceptible and is to be reliably communicated to a receiver.
Mycobacterium bovis BCG vaccine strains were compared with Mycobacterium tuberculosis by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. A 25-kDa protein observed in the BCG strains was absent in M. tuberculosis. Rabbit antibodies specific to the 25-kDa protein uniquely identified this protein in BCG strains but not in M. tuberculosis. It is suggested that the 25-kDa protein and polyclonal antibodies directed against this antigen can be exploited to distinguish BCG strains from M. tuberculosis.
The exponential growth of Internet traffic has made public servers increasingly vulnerable to unauthorized accesses and intrusions. In addition to maintaining low latency for the client, filtering unauthorized accesses has become one of the major concerns of a server maintainer. This implementation of an Intrusion Detection System distinguishes between the traffic coming from clients and the traffic originated from the attackers, in an attempt to simultaneously mitigate the problems of both latency and security. We then present the results of a series of stress and scalability tests, and suggest a number of potential uses for such a system. As computer attacks are becoming more and more difficult to identify the need for better and more efficient intrusion detection systems increases. The main problem with current intrusion detection systems is high rate of false alarms. Using honeypots provides effective solution to increase the security.
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