Sustainable Multi-Author Writing Style Analysis for Identifying Stylistic Differences Between Authors

Natural language processing (NLP) is a sustainable subfield of Artificial Intelligence that focuses on the interaction between computers and humans through natural language. NLP algorithms enable computers to comprehensively understand, interpret, and generate human language, thus facilitating the sustainable analysis and comprehension of vast amounts of textual data. Within the context of sustainable style change detection, NLP algorithms play a pivotal role in analyzing multi-author documents and identifying the points at which authors transition. This sustainable step is critical in authorship recognition as it furnishes a more precise comprehension of which sections were authored by different individuals. A multi-author document’s writing style can evolve over time, and this sustainability can prove invaluable in fields such as forensics, journalism, and literary studies, among others.The sustainable goal of this project is to investigate various NLP methods for sustainable style change detection. By scrutinizing datasets and juxtaposing them with advanced methodologies in the existing literature, the effectiveness of these strategies will be ascertained. The overarching aim of our study is to foster the progress of both the field of NLP research and its sustainable practical applications

Information Retrieval Based Solutions for Software Engineering Tasks Using C Codebases

Comments are descriptions of laws that a human can understand fluently. It’s easier to identify important law blocks with comments. But, not everyone can write the comments duly. They aren’t streamlined along with the law. Having outdated comments in the law can affect confusion rather than explanation. This paper aims at removing comments which aren’t related to code and not useful using Natural Language Processing( NLP). NLP has come one of the most habituated ways in the analysis of textbooks. In NLP, comments are written from the surrounding code given, and Machine Learning algorithms are applied. A semantic analysis frame for comments using textual and structural features grounded on comment orders and code–comment correlation. A machine learning approach is used to determine whether comments are also consistent and not superfluous based on code and comment correlation

Pneumonia Detection Using Deep Learning

Pneumonia is a life-threatening disease that affects the lungs in humans. Pneumonia is caused by Streptococcus pneumoniae bacterium. In pneumonia detection chest X-ray images are used as input dataset. To detect pneumonia chest X-rays need to be estimated by expert radiotherapists and it is expensive process, It would be beneficial and easy for people to use automatic system for pneumonia identification. People identify pneumonia using automatic system and get active treatment at early stages. The dataset containing chest X-ray images is obtained from Kaggle. The image characteristics are learned using pre-trained CNN(Convolutional Neural Network) models. CNN is used to analyse image features. This approach assists physicians in determining whether the patient has pneumonia or not. Early detection helps in early diagnosis

Conversational AI Chatbot for HealthCare

Health is a state of total physical, mental, and social wellbeing. chatbots have been applied to this industry frequently and in a variety ofways in the past, there is still room for more inventive uses. Healthcareconversational AI use cases are flexible and may be tailored to the industry. Patients might use them to gain additional knowledge about their disease, the therapies that are available, or even their insurance coverage. Because research has shown that healthcare chatbots can improve patient satisfaction and significantly reduce wait times, many healthcare organisations are considering incorporating them into their operations. Chatbots for healthcare can be used for a number of purposes, such as monitoring, anonymity, personalization, in-person involvement, and more. In this case study, the user's input on the patient's symptoms will be used to determine the patient's likely ailment type. According on the type of sickness, precautions will be suggested, and the patient will be sent to a doctor who specialises in that field. A sequential model was utilised to extract the text's symptoms, and the KNN method was then applied to predict the patient's ailment type.

Thermo-hydraulic investigation of two stepped micro pin fin heat sink having variable step size

To achieve enhanced flux dissipation, microchannels heat sink are being effectively implemented in various engineering processes. Here, a numerical analysis is to be carried out on an open stepped micro pin fin heat sink (MPFHS) with different step size. The stepped pin fin heat sink (PFHS) having varied fin height in an array of two fins having inline arrangement. For the simulation purpose, a single-phase water was used as working fluid with variable thermophysical properties. The present configuration has operated for Reynolds number =100-500, heat flux of 500 kW/m2. The step variation of 100 µm, 200 µm and 300 µm has been considered. It was observed that with increase in step size, the heat transfer augmentation also increases. However, at higher Reynold number, the present studies does not provide effective results in terms of sustainability

Neurocognitive Mechanisms of Anti-Lingual & Accent Bias Stereotyping in Virtual Reality and the Sustainable Development of Linguistic Response

Raising awareness through Virtual Experiencing (RAVE) and a Cross-Cultural Perspective on Raising Awareness through Virtual Experiencing (C-RAVE) approaches are used in this article to promote awareness of concerns relating to linguistic stereotyping. Virtual Reality (VR) technology has completely changed several facets of modern life, including instruction, training and entertainment. We give a summary of the techniques employed to increase awareness of how stereotypes might distort our vision. The patterns of the answers provided by the participants demonstrate that the reported gender of the speaker in addition to the accent (native vs. non-native), has an impact on the respondents' assessments of performance. Additionally, we were able to show that the discussions and ideas produced by these response patterns led to a rise in self-awareness of language and stereotyping-related problems. The paper gives a general description of RAVE and examines how it may improve education, create empathy and foster good social change. Focus on a critique of our methodologies and place our work within a larger debate of strategies and efforts for how educational institutions might actively contribute to combating (language) discrimination and bias in many ways. The relevance of Raising Awareness by Virtual Reality and Cross-Cultural Raising of Awareness by Virtual Reality in fostering inclusion and understanding in a society that is becoming more sustainable globally connected is highlighted in the article’s conclusion