Semantic-Based Classification of Toxic Comments Using Ensemble Learning

A social media is rapidly expanding, and its anonymity feature completely supports free speech. Hate speech directed at anyone or any group because of their ethnicity, clan, religion, national or cultural their heritage, sex, disability, gender orientation, or other characteristics is a violation of their authority. Seriously encourages violence or hate crimes and causes social unrest by undermining peace, trustworthiness, and human rights, among other things. Identifying toxic remarks in social media conversation is a critical but difficult job. There are several difficulties in detecting toxic text remarks using a suitable and particular social media dataset and its high-performance, selected classifier. People nowadays share messages not only in person, but also in online settings such as social networking sites and online groups. As a result, all social media sites and apps, as well as all current communities in the digital world, require an identification and prevention system. Finding toxic social media remarks has proven critical for content screening. The identifying blocker in such a system would need to notice any bad online behavior and alert the prophylactic blocker to take appropriate action. The purpose of this research was to assess each text and find various kinds of toxicities such as profanity, threats, name-calling, and identity-based hatred. Jigsaw's designed Wikipedia remark collection is used for this

Multi-Parameter Sensor Based Automation Farming

IOT innovation is used in the development of the Smart Farming Tracking the System. An Arduino Uno, a temperature humidity sensor, soil moisture sensor, water level sensor, water pumps, and DC motors strength this system. If the smart farming tracking system turns on, the sensors find the field’s water level and the soil’s moisture level. If the irrigation water stage falls below the level defined for a specific crop grown in the growing area, the irrigation system is going to start to pump water. The IOT warns concerning current level of water, soil moisture stage, and motor beginning will be shown on the LCD panel of the section. We are able to use the pumps by hand via a webpage. The farmers are additionally getting this data via mobile phone. By hitting a system- provided link, the individual using it may firmly prevent the water’s flow within the field. While carried out, the system will assist landowners to preserve suitable soil water and moisture levels, thus boosting yields with little work. The goal of this article is to identify grow illnesses and reduce losses in money. For picture appeal, we suggested an entirely based on deep learning method. We put the three most common Neural Network Designs to the test: Faster Region-based entirely judgment (SVM)Support Vector Machine Region-based entirely (RF) Random Forest method. The method suggested in the research can correctly detect many types of disease and is capable of dealing in complicated situations. In addition, the method may be expanded to recommend fertilizer according to extent evaluation as well as measurement. artificial intelligence (AI) entirely Machine Learning Response to this the combination the issue is a supervised categorization judgment

Video Transcript Summarizer

In today’s world, a large number of videos are uploaded in everyday, which contains information about something. The major challenge is to find the right video and understand the correct content, because there are lot of videos available some videos will contain useless content and even though the perfect content available that content should be required to us. If we not found right one it wastes your full effort and full time to extract the correct usefull information. We propose an innovation idea which uses NLP processing for text extraction and BERT Summarization for Text Summarization. This provides a video main content in text description and abstractive summary, enabling users to discriminate between relevant and irrelevant information according to their needs. Furthermore, our experiments show that the joint model can attain good results with informative, concise, and readable multi-line video description and summary in a human evaluation

Hamacher Maclaurin symmetric mean aggregation operators and WASPAS method for multiple criteria group decision making under T - spherical fuzzy environment

T-spherical fuzzy sets can accurately represent fuzzy information and effectively simulate real-world decision making scenarios by adjusting the parameter t. The Maclaurin symmetric mean can combine multiple arguments by considering the relationships between them in any decision making process. The main goal of this paper is to develop Maclaurin symmetric mean aggregation operators based on the Hamacher operations of T-spherical fuzzy sets. The developed operators are thoroughly examined through their fundamental properties. The defined operators are adopted to develop a decision making methodology called WASPAS (Weighted Aggregated Sum Product ASsessment) for solving multiple criteria group decision making problems in a T-spherical fuzzy environment. A real-life example of project assessment is illustrated to demonstrate the practicality of the proposed decision making approach. Sensitivity analysis of the parameters is carried out to check their effect on the decision results. A comparison analysis with existing methods confirms the accessibility of the developed approach

An evaluation of moral education's in enriching student's moral behaviour

This research aims to explore the capability of Moral Education in enriching student’s moral behaviour in Melaka secondary school through four research questions, 1) What are the policy, aims and objectives of Moral Education developed by the Ministry of Education in secondary schools?, 2) What are the scope and quality of support, resources, and training in the implementation of Moral Education?, 3) To what extent have the curriculum performance and monitoring, and instructional practices been employed in the implementation of Moral Education?, and 4) To what extent have the actual outcomes of Moral Education been achieved in secondary schools? Qualitative research design is applied in this research with the participation of two moral teachers and five upper secondary students. The data collection techniques apply in this research included document analysis and semi-structured interviews. The data collected are analyzed manually. This research discovered that the Moral Education is effective in enhancing students’ moral behavior. The syllabus of Moral Education is suitable for secondary school students. The curriculum should be continued with some improvement

Video Transcript Summarizer

In today’s world, a large number of videos are uploaded in everyday, which contains information about something. The major challenge is to find the right video and understand the correct content, because there are lot of videos available some videos will contain useless content and even though the perfect content available that content should be required to us. If we not found right one it wastes your full effort and full time to extract the correct usefull information. We propose an innovation idea which uses NLP processing for text extraction and BERT Summarization for Text Summarization. This provides a video main content in text description and abstractive summary, enabling users to discriminate between relevant and irrelevant information according to their needs. Furthermore, our experiments show that the joint model can attain good results with informative, concise, and readable multi-line video description and summary in a human evaluation

Multi-Parameter Sensor Based Automation Farming

IOT innovation is used in the development of the Smart Farming Tracking the System. An Arduino Uno, a temperature humidity sensor, soil moisture sensor, water level sensor, water pumps, and DC motors strength this system. If the smart farming tracking system turns on, the sensors find the field’s water level and the soil’s moisture level. If the irrigation water stage falls below the level defined for a specific crop grown in the growing area, the irrigation system is going to start to pump water. The IOT warns concerning current level of water, soil moisture stage, and motor beginning will be shown on the LCD panel of the section. We are able to use the pumps by hand via a webpage. The farmers are additionally getting this data via mobile phone. By hitting a system- provided link, the individual using it may firmly prevent the water’s flow within the field. While carried out, the system will assist landowners to preserve suitable soil water and moisture levels, thus boosting yields with little work. The goal of this article is to identify grow illnesses and reduce losses in money. For picture appeal, we suggested an entirely based on deep learning method. We put the three most common Neural Network Designs to the test: Faster Region-based entirely judgment (SVM)Support Vector Machine Region-based entirely (RF) Random Forest method. The method suggested in the research can correctly detect many types of disease and is capable of dealing in complicated situations. In addition, the method may be expanded to recommend fertilizer according to extent evaluation as well as measurement. artificial intelligence (AI) entirely Machine Learning Response to this the combination the issue is a supervised categorization judgment

Semantic-Based Classification of Toxic Comments Using Ensemble Learning

A social media is rapidly expanding, and its anonymity feature completely supports free speech. Hate speech directed at anyone or any group because of their ethnicity, clan, religion, national or cultural their heritage, sex, disability, gender orientation, or other characteristics is a violation of their authority. Seriously encourages violence or hate crimes and causes social unrest by undermining peace, trustworthiness, and human rights, among other things. Identifying toxic remarks in social media conversation is a critical but difficult job. There are several difficulties in detecting toxic text remarks using a suitable and particular social media dataset and its high-performance, selected classifier. People nowadays share messages not only in person, but also in online settings such as social networking sites and online groups. As a result, all social media sites and apps, as well as all current communities in the digital world, require an identification and prevention system. Finding toxic social media remarks has proven critical for content screening. The identifying blocker in such a system would need to notice any bad online behavior and alert the prophylactic blocker to take appropriate action. The purpose of this research was to assess each text and find various kinds of toxicities such as profanity, threats, name-calling, and identity-based hatred. Jigsaw's designed Wikipedia remark collection is used for this