scholarly journals Towards a Unified Sentiment Lexicon Based on Graphics Processing Units

2014 ◽  
Vol 2014 ◽  
pp. 1-19
Author(s):  
Liliana Ibeth Barbosa-Santillán ◽  
Inmaculada Álvarez-de-Mon y-Rego
Keyword(s):  
Graphics Processing Units ◽  
Pearson Correlation ◽  
Lexical Entry ◽  
A Value ◽  
Sentiment Lexicon ◽  
Unit Of Measurement ◽  
Time Required ◽  
Graphics Processing ◽  
High Processing

This paper presents an approach to create what we have called a Unified Sentiment Lexicon (USL). This approach aims at aligning, unifying, and expanding the set of sentiment lexicons which are available on the web in order to increase their robustness of coverage. One problem related to the task of the automatic unification of different scores of sentiment lexicons is that there are multiple lexical entries for which the classification of positive, negative, or neutral{P,N,Z}depends on the unit of measurement used in the annotation methodology of the source sentiment lexicon. Our USL approach computes the unified strength of polarity of each lexical entry based on the Pearson correlation coefficient which measures how correlated lexical entries are with a value between 1 and −1, where 1 indicates that the lexical entries are perfectly correlated, 0 indicates no correlation, and −1 means they are perfectly inversely correlated and so is the UnifiedMetrics procedure for CPU and GPU, respectively. Another problem is the high processing time required for computing all the lexical entries in the unification task. Thus, the USL approach computes a subset of lexical entries in each of the 1344 GPU cores and uses parallel processing in order to unify 155802 lexical entries. The results of the analysis conducted using the USL approach show that the USL has 95.430 lexical entries, out of which there are 35.201 considered to be positive, 22.029 negative, and 38.200 neutral. Finally, the runtime was 10 minutes for 95.430 lexical entries; this allows a reduction of the time computing for the UnifiedMetrics by 3 times.

scholarly journals The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video

2020 ◽  
Vol 12 (18) ◽  
pp. 3020
Author(s):  
Piotr Szymak ◽  
Paweł Piskur ◽  
Krzysztof Naus
Keyword(s):  
Deep Learning ◽  
World War Ii ◽  
Graphics Processing Units ◽  
Object Classification ◽  
Underwater Vehicles ◽  
Training Methods ◽  
Underwater Video ◽  
Deep Learning Neural Network ◽  
Graphics Processing

Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen for fifteen networks and the tests were provided with the description of the final results. The DLNNs were trained on servers with six medium class Graphics Processing Units (GPUs). Finally, the trained DLNN was implemented in the Nvidia JetsonTX2 platform installed on board of the BUV, and one of the network was verified in a real environment.

Rough assessment of GPU capabilities for parallel PCC-based biclustering method applied to microarray data sets

2015 ◽  
Vol 11 (4) ◽  
Author(s):  
Patryk Orzechowski ◽  
Krzysztof Boryczko
Keyword(s):  
Microarray Data ◽  
Graphics Processing Units ◽  
Pearson Correlation ◽  
Clustering Algorithms ◽  
Computation Time ◽  
Data Sets ◽  
Heterogeneous Environments ◽  
Heterogeneous Architecture ◽  
Input Matrix ◽  
Graphics Processing

AbstractParallel computing architectures are proven to significantly shorten computation time for different clustering algorithms. Nonetheless, some characteristics of the architecture limit the application of graphics processing units (GPUs) for biclustering task, whose function is to find focal similarities within the data. This might be one of the reasons why there have not been many biclustering algorithms proposed so far. In this article, we verify if there is any potential for application of complex biclustering calculations (CPU+GPU). We introduce minimax with Pearson correlation – a complex biclustering method. The algorithm utilizes Pearson’s correlation to determine similarity between rows of input matrix. We present two implementations of the algorithm, sequential and parallel, which are dedicated for heterogeneous environments. We verify the weak scaling efficiency to assess if a heterogeneous architecture may successfully shorten heavy biclustering computation time.

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification

2020 ◽  
Vol 4 (2) ◽  
pp. 377-383
Author(s):  
Eko Laksono ◽  
Achmad Basuki ◽  
Fitra Bachtiar
Keyword(s):  
Frequency Distribution ◽  
Confusion Matrix ◽  
High Accuracy ◽  
K Value ◽  
A Value ◽  
Optimal Value ◽  
Classification Evaluation ◽  
Email Spam ◽  
Email Classification

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

Ethnopharmacological Survey of Medicinal Plants Used in Traditional Treatment of Kidney Diseases in Fez–Meknes Region, Morocco

2019 ◽  
Vol 18 (2) ◽  
pp. 99-114
Author(s):  
M. Chebaibi ◽  
D. Bousta ◽  
I. Iken ◽  
H. Hoummani ◽  
A. Ech-Choayeby ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Medicinal Plants ◽  
Kidney Diseases ◽  
Rank Order ◽  
Pearson Correlation ◽  
Use Value ◽  
Traditional Treatment ◽  
Toxic Plants ◽  
A Value ◽  
Chi Squared

The purpose of this study was to inventory and collect information on plants and mixtures commonly used by herbalists to treat kidney disease in the Fez–Meknes region. We also aimed to compare the results obtained with the results of the other studies and exploit the correlations between different factors. An ethnopharmacological survey was conducted from 289 local herbalists in eight different areas of Fez–Meknes region. Ethnomedicinal uses and ethnobotanical indices were analyzed using quantitative tools, i.e., the total number of citation (TNC), use value (UV), family use value (FUV), fidelity level (FL), and rank order priority (ROP). Statistical analyses such as Pearson correlation and chi-squared test were performed to delineate any correlation. Two hundred and eighty-nine herbalists were questioned. Sixty-nine plant species belonging to 38 families were cited by herbalists for traditional treatment of kidney disease. The highest value of UV was obtained for Herniaria glabra L. (UV = 0.79), and Caryophyllaceae was the family frequently cited (FUV = 0.795). Ammodaucus leucotrichus Coss. & Dur. had the highest value of FL with a value of 100%, and the highest value of ROP was recorded for Herniaria glabra L. (ROP = 91%). Sociodemographic characteristics had a significant impact on the knowledge of toxic plants. Our study has revealed a cultural heritage linked to herbalism and a great wealth of medicinal plants, whose valorization and protection are necessary. Several studies are needed to sensitize herbalists and population on the danger of toxic plants, to extract chemical compounds from the main plants used, and to evaluate their toxicity.

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