The Classification System of Literary Works Based on K-Means Clustering

The plots in certain literary works are very complicated and hinder readers from understanding them. Therefore tools should be proposed to support readers; comprehension of complex literary works supports their understanding by providing the most important information to readers. A human reader must capture multiple levels of abstraction and meaning to formulate an understanding of a document. Hence, in this paper, an Improved [Formula: see text]-means clustering algorithm (IKCA) has been proposed for literary word classification. For text data, the words that can express exact semantic in a class are generally better features. This paper uses the proposed technique to capture numerous cluster centroids for every class and then select the high-frequency words in centroids the text features for classification. Furthermore, neural networks have been used to classify text documents and [Formula: see text]-mean to cluster text documents. To develop the model based on unsupervised and supervised techniques to meet and identify the similarity between documents. The numerical results show that the suggested model will enhance to increases quality comparison of the existing Algorithm and [Formula: see text]-means algorithm, accuracy comparison of ALA and IKCA (95.2%), time is taken for clustering is less than 2 hours, success rate (97.4%) and performance ratio (98.1%).

Character Chatbot - A Conversational AI Chatbot which can alter its Character according to User Specifications

A chatbot is a type of AI software, developed for the purpose of simulating a conversation with the user. They converse in natural language via messaging applications, websites, or any other form of communication platform. Some chatbots use extensive word-classification processes, Natural Language processors, and sophisticated AI, while others simply scan for general keywords and generate responses using common phrases obtained from an associated library or database. In our project, we have developed a model where the user can create a chatbot specific to a character. The user will be able to interact with the chatbot by giving it a few personality traits and the chatbot will be able to hold a conversation accordingly. If a personality is not specified then the chatbot has been given a default character - ‘Joey’ from the T.V. show ‘Friends’. We did this by using Simple Transformers Model along with the pre-trained model provided by Hugging Face. This includes the training dataset for various personality traits; but since we decided to give the chatbot a unique default character, we trained the model and fine-tuned it to a dataset we created ourselves in the same JSON format from every episode’s script of the show. Lastly, we used Tkinter package to build the GUI for the chatbot and connected it to our trained model and the chatbot was ready to chat

NEW VIEWS ON DIVIDING WORDS INTO CATEGORIES

This article provides information about the initial division of words into two groups, independent and auxiliary words, word combination, sentence, semantic-structure of words, lexeme, word classification. There are two important aspects of words to be considered in morphological classification in word categories.

MORPHO-PHONOLOGICAL EFFECTS IN GIKUYU LANGUAGE REDUPLICATION

This research is indispensable as it basically studies how Gikuyu language reduplication patterns can be explained using Prosodic Morphology Theory. It looks at the various types of reduplication in Gikuyu language and seeks to establish if reduplication in Gikuyu is considered morphological reduplication or phonological copying. Word classification as well as the Gikuyu vowels and consonants have extensively been discussed in this paper as a foundation for the reduplication discussion. The study tries to find out the logic worth of reduplication, how reduplication interconnect with morphological and phonological processes, linguistic units associated with this concept and draws conclusion that reduplication in Gikuyu is considered both morphological doubling and phonological copying. The study adopts Prosodic Morphology theoretical approach in reduplication patterns analysis. Gikuyu phonemic catalogue on vowels and consonants as well as the word categorization, that is, nouns, pronouns, verbs, adverbs and adjectives has been discussed as a foundation for the research. Reduplication is a morphological process in which there is repetition of a stem or a root of a word in Linguistics. Reduplication is important since it acts as a declension to bring out semantic roles such as lexical derivation, authentication and reinforcement to form new words. Qualitative sampling was done on the word categories and an outcome was established. There were various reduplication patterns in Gikuyu, several semantic patterns associated with it were listed, set out and reviewed. The findings have been scrutinized and analyzed for further recommendations. <p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0778/a.php" alt="Hit counter" /></p>

Denoising of EEG signal based on word imagination using ICA for artifact and noise removal on unspoken speech

<span>The purpose of this research is to observe the effectiveness of independent component analysis (ICA) method for denoising raw EEG signals based on word imagination, which will be used for word classification on unspoken speech. The electroencephalogram (EEG) signals are signals that represent the electrical activities of the human brain when someone is doing activities, such as sleeping, thinking or other physical activities. EEG data based on the word imagination used for the research is accompanied by artifacts, that come from muscle movements, heartbeat, eye blink, voltage and so on. In previous studies, the ICA method has been widely used and effective for relieving physiological artifacts. Artifact to signal ratio (ASR) is used to measure the effectiveness of ICA in this study. If the ratio is getting larger, the ICA method is considered effective for clearing noise and artifacts from the EEG data. Based on the experiment, the obtained ASR values from 11 subjects on 14 electrodes amounted are within the range of 0,910 to 1,080. Thus, it can be concluded that ICA is effective for removing artifacts from EEG signals based on word imagination.</span>

The Appraisals Used by Indonesian and Filipino Teachers in Casual Conversation

The Appraisal system is a comprehensive framework that prominently accommodates word classification. These words reflect the feelings and attitudes of the speakers that might implicitly be influenced by culture and norms. To find out the differences and similarities, transcribed conversations of Indonesian and Filipino teachers in Maria Regina School were analyzed. A qualitative method is applied to interpret the findings. The manifestation of attitude, judgment, and graduation are classified accordingly based on Martin and White (2005). The finding of this research is then supported and related to the values and culture that ground the way teachers express appraisal in conversation. The result indicates that teachers are dominant in the engagement system, especially entertain items. These items reflect kinship and open discussion among teachers through questions and the use of modals. In the attitude systems, Filipino teachers are more dominant in positive security that reflect confidence and togetherness while positive happiness items are found in Indonesian teachers where they express their fascination and contentment. Indonesian teachers tend to express judgment of normality while Judgement of capability was expressed more by Filipino teachers. In the graduation system, teachers mostly use force intensification to express the degree of intensity, repetition, and quality. The significance of the research is, the readers will learn to be more considerate in expressing their feeling and emotion in casual talk, especially with people who have different backgrounds of culture.