Browsing by Author "Ritika Singh"

Now showing 1 - 2 of 2

Herbal Drug-Loaded Nanoparticles for the Treatment of Neurodegenerative Diseases
(CRC Press, 2024) Soumya Katiyar; Shikha Kumari; Abhay Dev Tripathi; Ritika Singh; Pradeep K. Srivastava; Abha Mishra
Neurodegenerative disorders (NDs) such as Alzheimer’s disease, Parkinson’s disease, prion disease, spinal muscular atrophy, amyotrophic lateral sclerosis, Friedreich ataxia, and Huntington’s disease can severely affect or damage any brain areas. The exact cause and mechanism of NDs are unknown; however, various underlying molecular mechanisms and chemical processes have been proposed. Because of the presence of the blood-brain barrier (BBB), a tightly compacted system of blood arteries and endothelial cells that works to restrict the admittance of undesired substances into the brain, the approaches associated with the designing and advancement of new treatments for neurodegenerative illnesses are exceedingly complicated and demanding. Nanotechnology’s implementation and diverse progress offer promising potential for overcoming this issue. Nanotechnology has been one of the approaches that have changed human existence in numerous aspects and are an effective strategy that helps counteract the numerous restrictions of different illnesses, notably NDs. NPs can penetrate the BBB and deliver bioactive molecules to specific brain areas, reducing adverse reactions. Multiple therapeutic drugs might benefit from adding nanostructured materials such as polymeric/lipid nanoparticles (PNPs), nano-liposomes, nano-micelles, and carbon nanotubes (CNTs) to boost their effectiveness and performance along with minimizing adverse effects, prolonging the shelf life, and enhancing pharmacokinetics. The conjugates of nanoparticles (NPs) and traditional herbal plant bioactives (such as curcumin, berberine, quercetin, lycopene, thymoquinone, ferulic acid, and others) have recently gained importance in the creation of innovative neuro-therapeutics due to its universal availability, low cost, ability to allow potentially specific targeting of supply to the brain, and relatively low possibility for adverse outcomes. Furthermore, recent findings have shown that multiple plant-based compounds have remarkable neuroprotective, anti-oxidant, and reduced neuroinflammatory characteristics that help combat a wide range of NDs. This chapter discusses herbal therapeutics with remarkable potency in NDs, as well as documented herbal bioactive-loaded NP-based methods of delivery. © 2024 selection and editorial matter, Anurag Kumar Singh, Vivek K. Chaturvedi, and Jay Singh; individual chapters, the contributors.
Stock prediction using deep learning
(Springer New York LLC, 2017) Ritika Singh; Shashi Srivastava
Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. Methods applied in digital signal processing can be applied to stock data as both are time series. Similarly, learning outcome of this paper can be applied to speech time series data. Deep learning for stock prediction has been introduced in this paper and its performance is evaluated on Google stock price multimedia data (chart) from NASDAQ. The objective of this paper is to demonstrate that deep learning can improve stock market forecasting accuracy. For this, (2D)2PCA + Deep Neural Network (DNN) method is compared with state of the art method 2-Directional 2-Dimensional Principal Component Analysis (2D)2PCA + Radial Basis Function Neural Network (RBFNN). It is found that the proposed method is performing better than the existing method RBFNN with an improved accuracy of 4.8% for Hit Rate with a window size of 20. Also the results of the proposed model are compared with the Recurrent Neural Network (RNN) and it is found that the accuracy for Hit Rate is improved by 15.6%. The correlation coefficient between the actual and predicted return for DNN is 17.1% more than RBFNN and it is 43.4% better than RNN. © 2016, Springer Science+Business Media New York.