The aim and objective of this research are to create a model to measure the hate speech and to measure the contents of hate speech. The descriptive analysis method of data science was used to describe and summarize raw data from a dataset. We used Twitter as the social networking Web site for this research to analyze and measure the hate speech and its classifications. A dataset from kaggle datasets was applied for this research. To produce statistical results, we used monkey learn machine learning libraries which are incorporated with Python program to design and develop a model to classify and measure hate speech and its types that could be trained and tested using sentiment analysis. Researchers have found that the majority of the tweets are based on racist and ethnicity, sex and religion-based hate speech are also widely available.
In video surveillance, the attention human faces are frequently of small size. Image hallucination is an imperative factor disturbing the face classification by human and computer. In this paper, we propose a two-step face hallucination framework by means of training data sets which have a small quantity of low and high resolution images. In the first step, the global face is constructed based on optimal weights of training images. In the second step, a local residual compensation method bases on position patch via residual training face image data sets. Moreover, the hallucinated highresolution residual image which is obtained by the identical process can be subsequent for the local face. Finally, the hallucinated high-resolution residual image is appended with the input low-resolution face image which is interpolated to the high-resolution image dimension by an upsampling factor. Experiments fully demonstrate that our framework is very flexible and accomplishs good performance via small training data sets.
Breast cancer is a severe illness cause of mortality among females. In cancer diagnosis, accurate classification of breast cancer data is critical, and the distinction between malignant and benign tumors can help patients avoid unnecessary procedures. The categorization of breast cancer may also be used to determine the appropriate treatment choices. The categorization of patients into benign and malignant categories is a well-known medical research issue. Machine learning in Artificial Intelligence is commonly employed in predicting these types of cancer because it has the lead of finding important aspects from a medical data gathering. Several empirical types of research have used machine learning and soft computing techniques to treat breast cancer. Many people claim that their algorithms are better than others because they are faster, easier, or more accurate. Therefore, which algorithm is more accurate in classifying breast cancer was the research question. Furthermore, the major purpose of this research project is to calculate and evaluate the performance of SVM and RF algorithms in detecting breast cancer more correctly. The Wisconsin Breast Cancer Data Set (WBCD) is adopted for the empirical analysis. There are a total of 699 instances and 10 qualities to be examined. Based on the Accuracy, Reminder, Precision and F1 values, RF has the higher ratios in all four measurement scales with 92.98%, 93.65%, 88.05% and 90.67%, respectively. Therefore, RFs have the best probability of successfully diagnosing breast cancer.
On social networking sites, online hate speech has become more prevalent due to the quick expansion of mobile computing and Web technology. Previous research has found that being exposed to internet hate speech has substantial offline implications for historically disadvantaged communities. Therefore, there is a lot of interest in research on automated hate-based comment and post detection. Hate speech can have an influence on any population group, but some are more vulnerable than others. From this background, detecting and reporting such hate related comments and posts can help to avoid the harmful effects of hate speech. There are some studies available on this context and it was found that machine learning algorithms are more efficient in detecting abusive texts in social media. In this research, we applied selected seven machine learning algorithms such as Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boost (GB) and K Nearest Neighbor (KNN) to detect hate speech and compare the performances of those algorithms to develop an ensemble model. Researchers collected and combined Tamil – English code-mixed hate speech tweets dataset which was created in HASOC. This dataset's tweets are divided into two groups: not offensive and offensive. This dataset includes 35,442 tweets. In this research, NB has obtained highest F1 scores in detecting offensive and not offensive tweets with highest weighted average. But, SVM has obtained highest accuracy in detecting Tamil – English hate speech texts with 80 % in 10-fold cross validation. Based on the stand-alone performances, researchers developed two ensemble classifiers including max-voting and averaging ensemble. Averaging ensemble classification obtained 90.67% in accuracy. The research study's findings are significant because these results can be applied as a model for Tamil – English code-mixed hate speech to evaluate future research works using various algorithms for identifying hate contents more accurately and professionally.
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