In the field of Natural Language Processing, analyzing sentiment in Telugu presents distinctive challenges owing to the limited availability of annotated datasets. This paper presents a sophisticated method utilizing ...
详细信息
The domain of cross compiler development pertains to the process wherein code written in one programming language is translated to another so that it may be compatible with another environment and may take advantage o...
详细信息
This paper introduces a novel topic modeling framework that integrates graph-based representations with BERT embeddings to enhance semantic understanding and structural analysis of document collections. Traditional me...
详细信息
Urban energy scarcity and water inefficiency are critical issues in densely populated areas. This study addresses these challenges by integrating piezoelectric technology and IoT solutions. The system harnesses mechan...
详细信息
Over recent years conventional pattern recognition pattern recognition techniques have advanced significantly. Nonetheless, these methods heavily depend on manual feature extraction, potentially limiting the overall p...
详细信息
ISBN:
(数字)9798350383652
ISBN:
(纸本)9798350383652
Over recent years conventional pattern recognition pattern recognition techniques have advanced significantly. Nonetheless, these methods heavily depend on manual feature extraction, potentially limiting the overall performance and generalizability of the model. Given the rising prominence and efficiency of deep learning approaches, there has been a growing interest in leveraging these methods for human action recognition in mobile and wearable computing contexts. Despite this enthusiasm, existing deep learning architectures may not fully capitalize on the complex spatio-temprial patterns inherent in human actions. Moreover, deploying deep learning models in real-world scenarios often demands robustness, efficiency, and interpretability, which remain significant challenges. To address these issues, this paper proposes a novel approach: a deep neural network that integrates convolutional layers with long short-term memory(LSTM) units. By fusing the strengths of CNNs in spatial feature extraction with the temporal modelling capabilities of LSTMs, our hybrid architecture aims to enhance the accuracy and robustness of human activity recognition systems. This fusion enables the model to automatically learn hierarchical representations from data, eliminating the need for manual feature engineering. The approach holds, eliminating the need for manual feature engineering. The approach holds promise for applications in healthcare, sports analysis and assistive technology, where accurate and reliable HAR is essential. Overall, The proposed model achieves accuracy of 80% and 92% on the UCF50 dataset, a comprehensive video dataset, utilizing the CNN and LSTM models individually, respectively. These findings indicate that our hybrid model exhibits superior robustness and enhanced activity detection capabilities compared to certain previously reported results of only sensor data, surpassing an 90% accuracy threshold when trained on video-based data. Notably, the model demonstrate
Even with the growth of computerscience and availability of new areas of specialization, the problem of building compilers continues to be a core subject and offered at many universities around the world. This area i...
详细信息
The elevator control system serves as a crucial element in modern infrastructures, facilitating the vertical movement of individuals within buildings efficiently and securely. Finite State Machines (FSMs) offer a meth...
详细信息
Toxic comments are pervasive on online platforms, which makes it difficult to maintain positive dialogue and create a secure online space. People’s emotions and overall state of mental health start to seriously decli...
详细信息
In many countries lung cancer is acknowledged mostly as a deadly illness;it is recommended that the illness should be diagnosed early. Different medical Population Based information sources are employed in the analysi...
详细信息
Deep Fake technology has become increasingly sophisticated, posing a significant challenge to the integrity of digital content in today's information age. This research paper introduces a novel approach in detecti...
详细信息
暂无评论