Fault Prediction and Awareness for Power Distribution in Grid Connected RES Using Hybrid Machine Learning

Abstract The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data. 

Artificial Intelligence in Drug Identification and Validation: A Scoping Review

Abstract: The need to find new antibiotics is expanding as a result of the quick rise in bacteria that are resistant to medicines. Discovering drug-protein interactions could be an essential first step in the process of developing drugs since it will substantially reduce the scope of the look for possible solutions. Since in vitro assays are extremely time-consuming and pricey. We developed a machine learning method that can predict medications for the target in order to overcome this difficulty. We used the Padel script to do predictions on several chemical libraries, acquire drug physical and chemical properties, and obtain features extracted. establishing which model is best for predicting drug-target interactions is performed by analyzing the Random Forest technique with the Naive Bayes method, K-Nearest Neighbor, and other choices. This study reduces the failure rates and costs incurred when creating new pharmaceuticals while demonstrating the value of adopting machine learning approaches in drug discovery.

/pdf/drug-target-interaction-dti-and-prediction-using-machine-1jk2krde.pdf

Drug Target Interaction [DTI] and Prediction using Machine Learning

Abstract: Twitter is one of the most popular social media applications used by people of diverse age groups. Tweet has become an important mode of communication. Tweets are unstructured and often consist of different kinds of data tokens like mentions, hash tags, emoticons etc. Analysing the sentiment of this kind of unstructured data is challenging. In this paper, the experimentation of multiclass twitter sentiment analysis is presented, using three different approaches. Starting with the simple approach of frequency based feature extraction method, that helps capture the features from a text document by counting the frequency of the occurrence of words, to more advanced approaches like LSTM and BERT have been used. Different preprocessing techniques and different classifiers are explored in this paper. The performance of all the three methods is evaluated using metrics (precision, recall and f1 score). At the end the results of all three approaches are discussed and compared against one another. Keywords: NLP, BERT, LSTM, MLP Classifier, Multiclass Classification, Embeddings, Tweets.

/pdf/multi-class-classification-of-twitter-sentiments-using-25stylda.pdf

Multi-class Classification of Twitter Sentiments using Frequency based, LSTM and BERT Methods

India ranks number one for the number of road accident deaths caused across 199 countries as reported in the World Road Statistics, 2018 followed by China and US (Business standard, 2022). As per the WHO Global Report on Road Safety 2018, India accounts for almost 11% of the accident-related deaths in the World. Many of the fatal injuries could be avoided if the victim had quick access to the medicine. The aim of this project is to analyze and provide insights to the implementing party such as social entrepreneurs, start-ups or government agencies about the potential of installing First Aid Medicine Vending Machine in the accident-prone areas and in the locations having less access to medicine. In the event of a mishap at locations where therapeutic stores are not within the compass, First Aid Medicine Vending Machine ensures the availability of first aid kit 24x7. It has a QR code, which can be scanned by the customers to make payments for the first aid medicines received. The machine would be capable of accepting paper notes and also scan debit or credit cards. Also, it is proposed to provide the nearest hospital address and contact numbers on the machine. The project proposal includes: Identified accident zones in Bangalore city, Human Resource framework and strategies to implement the proposed plan, Marketing strategy and promotional plan to create awareness among consumers about the usage of First Aid Medicine Vending Machine

First Aid Medicine Vending Machine for Accident Zones of Bangalore City- An Analysis

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Papers

Drug Target Interaction [DTI] and Prediction using Machine Learning

Multi-class Classification of Twitter Sentiments using Frequency based, LSTM and BERT Methods

First Aid Medicine Vending Machine for Accident Zones of Bangalore City- An Analysis