Real-time subtitling of live video streams is still a major challenge because of the difficulties involved in processing spontaneous speech, dealing with multiple speakers, and coping with varying audio conditions wit...
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ISBN:
(数字)9798331538965
ISBN:
(纸本)9798331538972
Real-time subtitling of live video streams is still a major challenge because of the difficulties involved in processing spontaneous speech, dealing with multiple speakers, and coping with varying audio conditions with low latency. This research compares the performance of an end-to-end automatic subtitling system that integrates state-of-the-art automatic speech recognition (ASR) with sophisticated natural language processing. Processed 500 hours of live broadcast content in news, sports, and entertainment segments with a hybrid architecture that combined bidirectional LSTM networks and transformer models. Results indicate a 94.2%-word accuracy rate with an average latency of 1.2 seconds, which is a 27 % improvement over current systems. The system was especially strong in dealing with domain-specific vocabulary and subtitle synchronization during rapid speaker changes. Our results show that deep learning-based methods can attain near-human quality in real-time subtitle generation and can fulfill broadcast industry latency demands, rendering automatic subtitling ever more feasible for live shows.
The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver...
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ISBN:
(数字)9798331527549
ISBN:
(纸本)9798331527556
The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems.
machinelearning plays a virtual role in everyday speech commands, product recommendation, and even medical fields. But instead of providing better customer service, it provides safer autonomous vehicle systems. House...
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ISBN:
(数字)9798350375190
ISBN:
(纸本)9798350375206
machinelearning plays a virtual role in everyday speech commands, product recommendation, and even medical fields. But instead of providing better customer service, it provides safer autonomous vehicle systems. House price prediction using machinelearning is a hot topic in data science and AI. With the increasing availability of data and the advancement of machinelearning algorithms, accurately predicting house prices has never been easier. This study will not only assist housing developers and researchers to determine the most significant factors affecting housing prices but will also help determine the best machinelearning model to use in this research. This model will allow people to allocate resources to a request without relying on a broker. According to the results of this study, the random forest regression is the most accurate. Real estate market analysis is one of the most important tools for predicting changes in house prices and rental income. Several techniques have been developed in recent years to solve this problem, including statistical models and datamining methodologies and machinelearning algorithms. However, a few issues, including data quality and availability, needed to be fixed. Missing numbers, outliers, and inconsistent formats are common in housing databases. These issues could impair the performance of the prediction models. To solve these issues, thorough data preprocessing is required. Collaboration with data providers and governmental agencies is also required to ensure data coverage and accuracy.
Student success is important to institutions of learning. learning institutions offer academic support to their students to ensure success. Knowledge Tracing (KT), the task of modelling student knowledge based on thei...
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The article discusses the main tasks of machinelearning. The functional structure of a computer algorithm for solving machinelearning problems and a datamining model are considered. The solution of the simplest mac...
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Handwritten digit recognition presents a considerable challenge in patternrecognition due to the inherent variability in digit size, stroke width, orientation, and margin alignment. The difficulty is further exacerba...
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ISBN:
(数字)9798331518592
ISBN:
(纸本)9798331518608
Handwritten digit recognition presents a considerable challenge in patternrecognition due to the inherent variability in digit size, stroke width, orientation, and margin alignment. The difficulty is further exacerbated when different individuals write the same digit with varied stylistic nuances. Discriminating between visually similar digits, such as 1 and 7, 5 and 6, 3 and 8, 2 and 5, and 2 and 7, adds an additional layer of complexity. The offline handwritten digit recognition approach discussed in this paper leverages a range of machinelearning methodologies. The objective is to establish robust and accurate digit recognition techniques, utilizing several algorithms including Artificial Neural Networks, Convolutional Neural Networks, K-Nearest Neighbors, and Recurrent Neural Networks. These algorithms are evaluated using the Modified National Institute of Standards and Technology (MNIST) dataset and the US Postal Service (USPS) dataset to identify the most effective model. The selection of the optimal machinelearning approach is determined by factors such as dataset size, data complexity, and required accuracy. The Convolutional Neural Networks model demonstrated superior performance, achieving an accuracy of 98.58% with 10,000 MNIST images and 94.7% accuracy with 2007 USPS images when compared with other models.
This paper presents a Convolutional Neural Network (CNN) model developed for the automated identification of hand fractures from X-ray images, aiming to enhance diagnostic accuracy and efficiency in clinical settings....
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This paper explores the innovation of Web 3-enabled mental health chatbot, aimed at addressing the significant gaps in existing mental health solutions for the Indian demographic. The research identifies the cultural,...
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ISBN:
(数字)9798331538965
ISBN:
(纸本)9798331538972
This paper explores the innovation of Web 3-enabled mental health chatbot, aimed at addressing the significant gaps in existing mental health solutions for the Indian demographic. The research identifies the cultural, emotional, and privacyrelated challenges faced by Indian users when seeking mental health support. Current mental health platforms fail to provide culturally relevant solutions, lack emotional intelligence specific to Indian values and social structures, and often do not ensure adequate privacy. It aims to bridge these gaps by utilizing blockchain technology for decentralized authentication and enhanced privacy, ensuring user anonymity and data security. The chatbot is powered by machinelearning models, including BERT, and is designed to recognize and respond to emotional cues in Hindi and other regional languages, enabling more personalized and empathetic interactions. Additionally, the system incorporates features like games for relaxation and family photo recommendations to support emotional well-being. This research highlights the importance of integrating Web 3 technology and cultural context in mental health platforms to create a more accessible, private, and user-friendly mental health support system. The paper discusses the need for expanding mental health resources, optimizing AI models, and ensuring privacy in a manner that aligns with Indian societal values.
Neurodegenerative diseases are progressive disorders causing neuron damage, leading to cognitive and motor impairments. This study presents a machinelearning approach for classifying multiple neurological disorders u...
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ISBN:
(数字)9798331523893
ISBN:
(纸本)9798331523909
Neurodegenerative diseases are progressive disorders causing neuron damage, leading to cognitive and motor impairments. This study presents a machinelearning approach for classifying multiple neurological disorders using MRI data with the Orange data analytics software. A total of 165 MRI images representing conditions such as Alzheimer’s disease, Huntington’s disease, motor neuron disease, and Pick’s disease were sourced from the AANLIB database. Image features were extracted using the VGG-16 deep learning model, and classification was conducted via Support Vector machine (SVM) with polynomial, linear, radial basis function (RBF), and sigmoid kernels. The polynomial and sigmoid kernels achieved the highest classification accuracy of 93.3%, outperforming the linear and RBF kernels. These findings highlight that MRI-based classification using deep learning features and SVM in Orange can effectively differentiate neurological disorders, showing promise for early and accurate diagnosis.
Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac...
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ISBN:
(数字)9798350354171
ISBN:
(纸本)9798350354188
Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world’s biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various datamining and machinelearning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994.
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