Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylh...
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Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylhet, Bangladesh where transboundary water flows and climate change have increased the risk of disasters. Accurate flood detection plays a vital role in mitigating these impacts by allowing timely early warnings and strategic planning. Recent advancements in flood prediction research include the development of robust, accurate, and low-cost flood models designed for urban deployment. By applying and utilizing powerful deep learning models show promise in improving the accuracy of prediction and prevention. But those models faced significant issues related to scalability, data privacy concerns and limitations of cross-border data sharing including the inaccuracies in prediction models due to changing climate patterns. To address this, our research adopts the Federated Learning (FL) framework in an effort to train state-of-the-art deep learning models like Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Feed-Forward Neural Network (FNN) and Temporal Fusion Transformer-Convolutional Neural Network (TFT -CNN) on a 78-year dataset of rainfall, river flow, and meteorological variables from Sylhet and its upstream regions in Meghalaya and Assam, India. This approach promotes data privacy and allows collaborative learning while working under cross-border data-sharing constraints, therefore improving the accuracy of prediction. The results showed that the best-performing FNN model achieved an R-squared value of 0.96, a Mean Absolute Error (MAE) value of 0.02, Percent bias (PBIAS) value of 0.4185 and lower Root Mean Square Error (RMSE) in the FL environment. Explainable AI techniques, such as SHAP, sheds light on the most significant role played by upstream rainfall and river dynamics, particularly from Cherrapunji and the Surma-Kushiyara river system, in d
The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media *** social media sources contribute to the propagation of fake news that...
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The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media *** social media sources contribute to the propagation of fake news that has no real validity,but they accumulate over time and begin to appear in the feed of every consumer producing even more *** sustain the value of social media,such stories must be distinguished from the true *** a result,an automated system is required to save time and *** classification of fake news and misinformation from social media data corpora is the subject of this *** preprocessing and data improvement procedures are used to gather and preprocess two fake news *** text features are extracted using word embedding models Word2vec and Global Vectors for Word representation while textual features are extracted using n-gram approaches named Term Frequency-Inverse Document Frequency and Bag of Words from both datasets *** Encoder Representations from Transformers(BERT)is also employed to derive embedded representations from the input ***,three Machine Learning(ML)and two Deep Learning(DL)algorithms are utilized for fake news *** also carries out the classification of embedded outcomes generated by it in parallel with the ML and DL *** terms of overall performance,the DL-based Convolutional Neural Network stands out in the case of the first while BERT performs better in the case of the second dataset.
Parameter control involves dynamically adjusting the parameter values of the evolutionary algorithm throughout the optimization process, including parameters like mutation rate and operator selection. Self-adaptation ...
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Community question and answer (Q&A) websites have become invaluable information and knowledge-sharing sources. Effective topic modelling on these platforms is crucial for organising and navigating the vast amount ...
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Sentiment analysis is used to get meaningful insights from data that is being retrieved from various resources or social media platforms. Sentiment analysis plays an important role in making crucial decisions that can...
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The increasing incidence of vehicle-animal collisions poses significant risks to both human and wildlife safety. To address this challenge, the implementation of IoT (Internet of Things) sensor networks for wild-anima...
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The online food order and delivery service is becoming a very popular web-based application in Bangladesh. These days, a growing number of individuals are interested in ordering a variety of foods online. In addition,...
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In a cloud environment,graphics processing units(GPUs)are the primary devices used for high-performance *** exploit flexible resource utilization,a key advantage of cloud *** users share GPUs,which serve as coprocesso...
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In a cloud environment,graphics processing units(GPUs)are the primary devices used for high-performance *** exploit flexible resource utilization,a key advantage of cloud *** users share GPUs,which serve as coprocessors of central processing units(CPUs)and are activated only if tasks demand GPU *** a container environment,where resources can be shared among multiple users,GPU utilization can be increased by minimizing idle time because the tasks of many users run on a single ***,unlike CPUs and memory,GPUs cannot logically multiplex their ***,GPU memory does not support over-utilization:when it runs out,tasks will ***,it is necessary to regulate the order of execution of concurrently running GPU tasks to avoid such task failures and to ensure equitable GPU sharing among *** this paper,we propose a GPU task execution order management technique that controls GPU usage via time-based *** technique seeks to ensure equal GPU time among users in a container environment to prevent task *** the meantime,we use a deferred processing method to prevent GPU memory shortages when GPU tasks are executed simultaneously and to determine the execution order based on the GPU usage *** the order of GPU tasks cannot be externally adjusted arbitrarily once the task commences,the GPU task is indirectly paused by pausing the *** addition,as container pause/unpause status is based on the information about the available GPU memory capacity,overuse of GPU memory can be prevented at the *** a result,the strategy can prevent task failure and the GPU tasks can be experimentally processed in appropriate order.
Advancements in language models (LMs) have sparked interest in exploring their potential as knowledge bases (KBs) due to their high capability for storing huge amounts of factual knowledge and semantic understanding. ...
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A sin laryngeal carcinoma is the most common kind of head and neck cancer to damage the soft tissues of the larynx. To prevent further medical difficulties and to provide better patient care, early stage laryngeal can...
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