An Implementation of Machine Learning Algorithm for Fake News Detection

Fake news is a growing concern in the age of social media, as it can spread rapidly and have serious consequences. To combat this issue, machine learning techniques have been used for fake news detection. In this study, we propose two models, LSTM and SVM, for fake news detection. The LSTM model is a deep learning algorithm that is particularly suited to sequential data such as text. It can capture long-term dependencies in the text and has shown promising results in natural language processing tasks. The SVM model, on the other hand, is a classical machine learning algorithm that has been widely used for classification tasks. To evaluate the performance of the proposed models, we conducted experiments on a dataset of news articles. Our results show that both models achieve high accuracy in detecting fake news. However, the LSTM model outperforms the SVM model with an accuracy of 94% compared to 89%. Furthermore, we conducted a feature importance analysis to determine the most important features for detecting fake news. The results show that the presence of certain words and phrases, such as "unverified" and "anonymous sources", are strong indicators of fake news. In conclusion, our study demonstrates the effectiveness of using machine learning techniques, particularly LSTM and SVM, for detecting fake news. This research can be applied to assist individuals and organizations in identifying and combating fake news in the digital age

Some Directions beyond Traditional Quantum Secret Sharing

We investigate two directions beyond the traditional quantum secret sharing (QSS). First, a restriction on QSS that comes from the no-cloning theorem is that any pair of authorized sets in an access structure should overlap. From the viewpoint of application, this places an unnatural constraint on secret sharing. We present a generalization, called assisted QSS (AQSS), where access structures without pairwise overlap of authorized sets is permissible, provided some shares are withheld by the share dealer. We show that no more than $\lambda-1$ withheld shares are required, where $\lambda$ is the minimum number of {\em partially linked classes} among the authorized sets for the QSS. Our result means that such applications of QSS need not be thwarted by the no-cloning theorem. Secondly, we point out a way of combining the features of QSS and quantum key distribution (QKD) for applications where a classical information is shared by quantum means. We observe that in such case, it is often possible to reduce the security proof of QSS to that of QKD.Comment: To appear in Physica Scripta, 7 pages, 1 figure, subsumes arXiv:quant-ph/040720

Ethnobotanical notes on some potential wild edible fruits used by the Bhotia community of Niti Valley, Uttarakhand, India

135-144The present study deals with the diversity, distribution and ethnobotany of wild edible fruits consumed by the Bhotia tribe of Niti valley, Chamoli, Uttarakhand, India. Most of these valuable resources fulfil the nutritional, health and economic requirements of Bhotia community up to a large extent. Though the wild edible fruits have great socio-economic significance in sustenance of many tribal/rural communities but due to the increasing trends of modernized agriculture, climate change and various natural and anthropogenic hazards, a large number of genetic resources of wild edible fruits are under severe threat, hence warrants immediate attention for their conservation, domestication and utilization through improvement/selection. Keeping this in view, ethnobotanical knowledge and diversity distribution patterns of 27 potential wild edible fruits consumed by Bhotia tribe in Niti Valley have been recorded with their botanical names, family, local names, uses and indigenous processing methods

Khesari (Lathyrus sativus L.), an ancient legume for future gain: An expedition collection from parts of West Bengal state of Eastern India

395-403Grasspea is one of the staple foods of the local people living in the eastern parts of India. An expedition was undertaken during March, 2020 to collect germplasm of grasspea in the lower-Gangetic riverine belt and coastal areas of West Bengal of eastern India lying between latitude 21.43-24.44°N and longitude 87.23-88.90°E. From the results of a structured questionnaire administered to grasspea farmers in 57 villages located in 96 local government areas, it appeared that grasspea is the primary winter pulse cultivated in this region. Large variability of germplasm exist, ranging from small to bold seed, early to late maturing types, moderate to the high biomass type of grasspea. Most of these landraces have been adopted over the years from neighbouring communities, but in a few instances, the varietal replacement was noted, which came either through the involvement of government departments or local seed dealers. The highest proportion of the accessions (52.38%) was collected from the Purba Medinipur district, and the lowest (19.05%) was from Paschim Medinipur. On-spot evaluation of morphological traits, variations was detected in the descriptor characteristics across the locations. A total of 21 accessions was collected and assessed on-spot for different characters, viz., the seed's size, shape, seed colour, taste and texture revealed significant variation. The implications of this survey results for grasspea improvement in India are discussed in the present study

Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice

Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R2 = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant

Azimuthal anisotropy of charged jet production in root s(NN)=2.76 TeV Pb-Pb collisions

We present measurements of the azimuthal dependence of charged jet production in central and semi-central root s(NN) = 2.76 TeV Pb-Pb collisions with respect to the second harmonic event plane, quantified as nu(ch)(2) (jet). Jet finding is performed employing the anti-k(T) algorithm with a resolution parameter R = 0.2 using charged tracks from the ALICE tracking system. The contribution of the azimuthal anisotropy of the underlying event is taken into account event-by-event. The remaining (statistical) region-to-region fluctuations are removed on an ensemble basis by unfolding the jet spectra for different event plane orientations independently. Significant non-zero nu(ch)(2) (jet) is observed in semi-central collisions (30-50% centrality) for 20 <p(T)(ch) (jet) <90 GeV/c. The azimuthal dependence of the charged jet production is similar to the dependence observed for jets comprising both charged and neutral fragments, and compatible with measurements of the nu(2) of single charged particles at high p(T). Good agreement between the data and predictions from JEWEL, an event generator simulating parton shower evolution in the presence of a dense QCD medium, is found in semi-central collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV

Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe