In this study, we propose the gesture recognition algorithm using support vector machines (SVM) and histogram of oriented gradient (HOG). Besides, we also use the CNN model to classify gestures. We approach and select techniques of applying problem controlling for the robotic system. The goal of the algorithm is to detect gestures with real-time processing speed, minimize interference, and reduce the ability to capture unintentional gestures. Static gesture controls are used in this study including on, off, increasing, and decreasing. Besides, it uses motion gestures including turning on the status switch and increasing and decreasing the volume. Results show that the algorithm is up to 99% accuracy with a 70-millisecond execution time per frame that is suitable for industrial applications.
Sentiment classification has been used in many different fields because it has many significant contributions in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a new model for big data sentiment classification by using a combination of an unsupervised learning algorithm of a machine learning with a Ruzicka Coefficient (RC) in this work. A Self-Organizing Map Algorithm (SOM) of the machine learning is used in clustering the documents of the testing data set (TES) comprising 7,500,000 documents, which are the 3,750,000 positive and the 3,750,000 negative in English, into either the positive group or the negative group of our training data set (TRA) which is 3,000,000 sentences including the 1,500,000 positive sentences and the 1,500,000 negative sentences in English. In this study, we do not use a vector space modeling (VSM). We do not use any multi-dimensional vectors according to both the VSM and many sentiment lexicons. We use many sentiment lexicons of our basis English sentiment dictionary (bESD). We use many one-dimensional vectors based on the sentiment lexicons. We use a similarity coefficient in this study. We do not use any one-dimensional vectors based on the VSM. We have achieved 88.64% accuracy of the TES. The execution time of the proposed model in a distributed network environment-DNE is less than that in a sequential system-SS. Many commercial applications and surveys of the sentiment classification can widely use the results of the proposed model.
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