The advancement of applications based on various Deep Neural Network (DNN) architectures, such as Large Language Models (LLMs) has led to an increasing need for the efficient utilization of network resources to meet t...
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With the development of voice control technology, a new page has opened in the history of human-computer interaction. Voice assistants are one of the most important technologies in our daily lives because they enable ...
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Portable sinks assume a vital part in information assortment within wireless sensor networks (WSNs), offering dynamic and flexible solutions to address the limitations of static sink-based architectures. Unlike their ...
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Detecting AI-generated text in the field of academia is becoming very prominent. This paper presents a solution for Task 2: AI vs. Human - Academic Essay Authenticity Challenge in the COLING 2025 DAIGenC Workshop1. Th...
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Network intrusion remains a critical security concern in the modern cyber world, necessitating innovative approaches to enhance network confidentiality. In this paper, we introduce a novel Hybrid Genetic Coati-Pelican...
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Recent advancements in Visual Question Answering (VQA) have been driven by the integration of complex attention mechanisms. This work introduces a novel approach aimed at enhancing multi-modal representations through ...
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Intelligent Vehicular Ad Hoc Networks which integrates deep learning techniques with modern vehicular communication networks can play a major role in prediction of vehicular traffic as well as efficient dissemination ...
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Deep convolutional neural network architectures have in recent years been widely used for enhancing various computer vision tasks, such as Image classification, Semantic Segmentation and Object detection. With great a...
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This paper has the sole purpose of showing the fact that religious sentiment detection holds an important place in the industry and our efforts have been totally concerned with easing the problems of the industry whic...
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ISBN:
(纸本)9783031488757
This paper has the sole purpose of showing the fact that religious sentiment detection holds an important place in the industry and our efforts have been totally concerned with easing the problems of the industry which have been existing to date. Our research has been focused on the shortcomings of various previous methods which have been suggested by previous researchers to classify religious sentiments. There has never been any single application that can classify the sentiments present in a given block of religious text by analyzing only the religious text. We have designed the model in such a way that the users will not have to specify the religious texts and filter them out. The application will reject all the nonreligious texts and provide the desired outcome after analyzing the given religious text which is provided by the user. The model works on the basis of Natural Language Processing and it is able to handle a large amount of data. It is trained using the data sets of the 12 main religions of the world and it is able to perform predictive analysis of the input text since the model is trained using RNN and LSTM algorithms. We have also used the KNN algorithm in the testing phases of the model. A brief analysis of the time complexity along with the comparison of performance evaluation among the different methods have also been discussed in this paper. In the results that we received, it can be clearly seen that we have achieved a minimum loss of 0.083, and the highest accuracy value of the model is found to be 99.8%, This study evaluates the different approaches that can be used to perform sentiment analysis on religious texts and provides a landmark for future researchers to continue improvements in this field. Our research paves the way for future researchers to work more on the untouched portions of sentiment analysis and its applications in real life. In this way, these extensive technologies can be put to better use. We believe that our work and our re
The increasing use of Romanized typing for Indo-Aryan languages on social media poses challenges due to its lack of standardization and loss of linguistic richness. To address this, we propose a sentence-level back-tr...
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