Aflood is a significant damaging natural calamity that causes loss of life and *** work on the construction offlood prediction models intended to reduce risks,suggest policies,reduce mortality,and limit property damage c...
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Aflood is a significant damaging natural calamity that causes loss of life and *** work on the construction offlood prediction models intended to reduce risks,suggest policies,reduce mortality,and limit property damage caused byfl*** massive amount of data generated by social media platforms such as Twitter opens the door toflood *** of the real-time nature of Twitter data,some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue ***,due to the shorter duration of Tweets,it is difficult to construct a perfect prediction model for determiningfl*** learning(ML)and deep learning(DL)approaches can be used to statistically developflood prediction *** the same time,the vast amount of Tweets necessitates the use of a big data analytics(BDA)tool forflood *** this regard,this work provides an optimal deep learning-basedflood forecasting model with big data analytics(ODLFF-BDA)based on Twitter *** suggested ODLFF-BDA technique intends to anticipate the existence offloods using tweets in a big data *** ODLFF-BDA technique comprises data pre-processing to convert the input tweets into a usable *** addition,a Bidirectional Encoder Representations from Transformers(BERT)model is used to generate emotive contextual embed-ding from ***,a gated recurrent unit(GRU)with a Multilayer Convolutional Neural Network(MLCNN)is used to extract local data and predict thefl***,an Equilibrium Optimizer(EO)is used tofine-tune the hyper-parameters of the GRU and MLCNN models in order to increase prediction *** memory usage is pull down lesser than 3.5 MB,if its compared with the other algorithm *** ODLFF-BDA technique’s performance was validated using a benchmark Kaggle dataset,and thefindings showed that it outperformed other recent approaches significantly.
When the ground communication base stations in the target area are severely destroyed,the deployment of Unmanned Aerial Vehicle(UAV)ad hoc networks can provide people with temporary communication ***,it is necessary t...
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Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-...
Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].
In the massive Machine-Type Communication (mMTC), the exponential growth of Internet of Things (IoT) devices over Low Power Wide Area Networks (LPWANs) presents substantial issues regarding energy efficiency and stabi...
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This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation...
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Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhea...
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This research paper presents a novel algorithmic approach for character recognition and contextual analysis of temple inscriptions, specifically focusing on Tamil ancient script. The methodology combines advanced prep...
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This research paper presents a novel algorithmic approach for character recognition and contextual analysis of temple inscriptions, specifically focusing on Tamil ancient script. The methodology combines advanced preprocessing techniques, deep learning models, and contextual analysis to address the challenges posed by noisy images, script variations, and historical context understanding. We compiled a dataset of 100 high-resolution images of temple inscriptions from various regions and periods. The preprocessing phase involves Noise Reduction, Contrast Enhancement, Orientation Correction, and Adaptive Binarization algorithms to enhance the quality of the inscription images. The character recognition stage employs Convolutional Neural Networks with Transfer Learning, further enhanced by the Multi-head Attention mechanism in Vision Transformers (ViT). The character segmentation algorithm used was the Stroke Width Transform. Transfer Learning was incorporated to adapt the pre-trained ViT model to our specific task. This approach significantly improves the model’s ability to recognize characters from diverse scripts and languages. The results demonstrate the effectiveness of the proposed methodology. The character recognition accuracy metrics include a precision of 97.25%, a recall of 95.05%, and an F1-score of 95.17%. Additionally, the model achieved a recognition rate of 98.92% for key terms related to historical events, deities, and rulers. It also demonstrated a 94% recognition rate for context-specific phrases and a 95% recognition rate for historical dates. Contextual analysis results indicate that the model successfully identifies specific terms, phrases, and historical references, contributing to a deeper understanding of the inscriptions. The model's ability to recognize characters from multiple scripts underscores its adaptability to diverse inscriptions. In conclusion, this research provides a comprehensive and efficient solution for character recognition and
ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and co...
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An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric an...
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The main aim of this project is to teach the computer to recognize patterns in air quality data, such as how pollution levels change over time. We use the LSTM network to make sense of different things like pollution ...
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