Experimental design and machine learning strategies for parameters screening and optimization of Hantzsch condensation reaction for the assay of sodium alendronate in oral solution

RSC Advances ◽  
2015 ◽  
Vol 5 (9) ◽  
pp. 6385-6394 ◽  
Author(s):  
Mohamed A. Korany ◽  
Marwa A. A. Ragab ◽  
Rasha M. Youssef ◽  
Mostafa A. Afify
Keyword(s):  
Machine Learning ◽  
Experimental Design ◽  
Learning Strategies ◽  
Condensation Reaction ◽  
Oral Solution ◽  
Chemical Derivatization ◽  
Reaction Parameters ◽  
Hantzsch Condensation ◽  
Optimum Reaction

An experimental design was adopted to attain the optimum reaction parameters of chemical derivatization of anhydrous sodium alendronate in an oral solution formulaviaHantzsch condensation reaction.

Determination of midodrine hydrochloride via Hantzsch condensation reaction: a factorial design based spectrophotometric approach

RSC Advances ◽  
2015 ◽  
Vol 5 (60) ◽  
pp. 48474-48483 ◽  
Author(s):  
Marwa S. Elazazy
Keyword(s):  
Experimental Design ◽  
Factorial Design ◽  
Condensation Reaction ◽  
Current Approach ◽  
Hantzsch Condensation

An experimental design was adopted for determination of MD·HCl. The novelty of the current approach arises from being multivariate compared to traditional univariate techniques.

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

scholarly journals Machine Learning Strategies When Transitioning between Biological Assays

2021 ◽  
Author(s):  
Staffan Arvidsson McShane ◽  
Ernst Ahlberg ◽  
Tobias Noeske ◽  
Ola Spjuth
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Biological Assays

scholarly journals Preventive and Curative Personality Profiling Based on EEG, ERP, and Big Five Personality Traits: A Literature Review

2020 ◽  
Vol 17 (2) ◽  
pp. 531-545
Author(s):  
Cut Amalia Saffiera ◽  
Raini Hassan ◽  
Amelia Ritahani Ismail
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Experimental Design ◽  
Personality Traits ◽  
Big Five ◽  
Healthy Lifestyle ◽  
Big Five Personality Traits ◽  
Event Related Potential ◽  
Big Five Personality ◽  
Electrophysiological Signals

Healthy lifestyle is a significant factor that impacts on the budget for medicine. According to psychological studies, personality traits based on the Big Five personality traits especially the neuroticism and conscientiousness, have the ability to predict healthy lifestyle profiling. Electrophysiological signals have been used to explore the nature of individual differences and personality that are related to perception. In this paper, we reviewed studies examining healthy lifestyle profile i.e., preventive and curative using electroencephalography (EEG) and event-related potential (ERP) signals. This study proposed a general experimental model by reviewing the literature to build suitable experimental design for implementing artificial intelligence techniques based on the machine learning.

scholarly journals Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies

2018 ◽  
Vol 20 (3) ◽  
pp. 302-320 ◽  
Author(s):  
Elisa Cuadrado-Godia ◽  
Pratistha Dwivedi ◽  
Sanjiv Sharma ◽  
Angel Ois Santiago ◽  
Jaume Roquer Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Vessel Disease

scholarly journals Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”

Science ◽  
2018 ◽  
Vol 362 (6416) ◽  
pp. eaat8603 ◽  
Author(s):  
Kangway V. Chuang ◽  
Michael J. Keiser
Keyword(s):  
Machine Learning ◽  
Experimental Design ◽  
Coupling Reaction ◽  
Cross Coupling ◽  
Learning Models ◽  
Applied Machine Learning ◽  
Cross Coupling Reaction ◽  
Reaction Performance ◽  
Test Scenarios ◽  
Machine Learning Models

Ahneman et al. (Reports, 13 April 2018) applied machine learning models to predict C–N cross-coupling reaction yields. The models use atomic, electronic, and vibrational descriptors as input features. However, the experimental design is insufficient to distinguish models trained on chemical features from those trained solely on random-valued features in retrospective and prospective test scenarios, thus failing classical controls in machine learning.

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