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A Review of Herbal Technology

A Review of Herbal Technology/strong>
Authors:-Averineni Ravi Kumar N, Deepa Ramani

Abstract- Herbal Drug Development Plant Selection and Identification The first step is identifying a plant with potential medicinal properties. Ethnobotanical surveys, historical use, and scientific literature guide this process. Extraction and Isolation of Active Constituents Different extraction methods (e.g., solvent extraction, steam distillation, supercritical fluid extraction) are employed to isolate the active ingredients from plant material. Techniques like chromatography and spectroscopy are used to identify and purify these compounds. Standardization Standardization ensures that a herbal product contains a consistent amount of active compounds in each batch. This is crucial for reproducibility and efficacy. Preclinical Studies Laboratory testing on animals and in vitro models to assess the biological activity, toxicity, and pharmacokinetics of the herbal product. Clinical Trials Human trials are conducted to evaluate the safety, efficacy, and dosage of the herbal drug. Technological Approaches in Herbal Drug Development Extraction Techniques Solvent Extraction The most common method, where solvents like ethanol or water are used to extract bioactive compounds. Supercritical Fluid Extraction (SFE) Uses supercritical CO2 as a solvent, offering a cleaner and more efficient extraction method. Microwave-Assisted Extraction (MAE) Uses microwave energy to enhance the efficiency of the extraction process. Ultrasonic Extraction Utilizes high-frequency sound waves to enhance solvent penetration and compound release. Formulation Development Herbal products may be formulated into various forms

DOI: 10.61137/ijsret.vol.10.issue6.405

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AI Based Smart Energy Meter for Data Analytics

AI Based Smart Energy Meter for Data Analytics/strong>
Authors:-Assistant Professor Mrs.B. Christyjuliet, Dinesh Kumar.B, Divya.G, Kaviraj.S, Monisha.R

Abstract- The proliferation of smart meter technology offers vast opportunities for harnessing real-time data to optimize energy consumption, predict demand, and support sustainable energy grids. This paper explores the integration of artificial intelligence (AI) techniques, such as machine learning and deep learning, into smart meter data analytics, enhancing the accuracy of predictions and anomaly detection. With the rise of big data from millions of connected devices, AI-based analytics are vital to efficient energy management. We present a comparative analysis of various AI models used for smart meter data analytics and propose improvements for their real-time applications.

DOI: 10.61137/ijsret.vol.10.issue6.404

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