Machine learning approaches to predict adsorption capacity of Azolla pinnata in the removal of methylene blue

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

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Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

10.26434/chemrxiv.5513581.v1 ◽

2017 ◽

Author(s):

Sabrina Jaeger ◽

Simone Fulle ◽

Samo Turk

Keyword(s):

Machine Learning ◽

Language Processing ◽

Supervised Machine Learning ◽

Learning Approach ◽

Learning Approaches ◽

Unsupervised Machine Learning ◽

Feature Representations ◽

Machine Learning Approach ◽

The Individual ◽

Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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DETECTION OF ANOMALY BASED APPLICATION LAYER DDoS ATTACKS USING MACHINE LEARNING APPROACHES

i-manager s Journal on Computer Science ◽

10.26634/jcom.4.2.8120 ◽

2016 ◽

Vol 4 (2) ◽

pp. 6

Author(s):

VANI NIDHI M.S.P.S. ◽

PRASAD K. MUNIVARA ◽

◽

Keyword(s):

Machine Learning ◽

Learning Approaches ◽

Ddos Attacks ◽

Application Layer

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Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

10.31234/osf.io/eqcdx ◽

2019 ◽

Author(s):

Oskar Flygare ◽

Jesper Enander ◽

Erik Andersson ◽

Brjánn Ljótsson ◽

Volen Z Ivanov ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forests ◽

Clinical Utility ◽

Body Dysmorphic Disorder ◽

Prediction Models ◽

Behavioral Therapy ◽

Learning Approach ◽

Learning Approaches ◽

Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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Identification of interface residues involved in protein-protein and protein-DNA interactions from sequence using machine learning approaches

10.31274/rtd-180813-2240 ◽

2005 ◽

Author(s):

Changhui Yan

Keyword(s):

Machine Learning ◽

Learning Approaches ◽

Dna Interactions ◽

Protein Dna Interactions ◽

Interface Residues

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Analisis Variasi Temperatur Aktivasi Terhadap Daya Serap Arang Aktif Tandan Aren (Arenga Pinnata Merr) Dengan Agen Aktivasi Potassium Silicate(K2SiO3)

Jurnal Penelitian Pendidikan Fisika ◽

10.36709/jipfi.v5i3.13803 ◽

2020 ◽

Vol 5 (3) ◽

pp. 221

Author(s):

Muhammad Azam ◽

Muhammad Anas ◽

Erniwati Erniwati

Keyword(s):

Methylene Blue ◽

Activated Carbon ◽

Adsorption Capacity ◽

Temperature Variation ◽

Chemical Activation ◽

Weight Ratio ◽

Potassium Silicate ◽

Physical Activation ◽

Activation Temperature ◽

Pyrolysis Reactor

This study aims to determine the effect of variation of activation temperature of activated carbon from sugar palm bunches of chemically activatied with the activation agent of potassium silicate (K2SiO3) on the adsorption capacity of iodine and methylene blue. Activated carbon from bunches of sugar palmacquired in four steps: preparationsteps, carbonizationstepsusing the pyrolysis reactor with temperature of 300 oC - 400 oC for 8 hours and chemical activation using of potassium silicate (K2SiO3) activator in weight ratio of 2: 1 and physical activation using the electric furnace for 30 minutes with temperature variation of600 oC, 650 oC, 700 oC, 750 oC and 800 oC. The iodine and methyleneblue adsorption testedby Titrimetric method and Spectrophotometry methodrespectively. The results of the adsorption of iodine and methylene blue activated carbon from sugar palm bunches increased from 240.55 mg/g and 63.14 mg/g at a temperature of 600 oC to achieve the highest adsorption capacity of 325.80 mg/g and 73.59 mg/g at temperature of 700 oC and decreased by 257.54 mg/g and 52.03 mg/g at a temperature of 800 oCrespectively.However, it does not meet to Indonesia standard (Standard Nasional Indonesia/SNI), which is 750 mg/g and 120 mg/g respectively.

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