Methodologies serve as the foundation for managing and organizing the software development process, encompassing diverse approaches like feature-driven development, waterfall, and extreme programming. In addressing ch...
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Inverse problems arise in many applied sciences and industrial design by providing parameters that can not be measured directly. Classical numerical methods exhibit instability issues for these problems. In this paper...
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With the advancement of deep learning technology, researchers have begun employing deep neural network models such as LSTM, MLP, CNN, and GRU to tackle nonlinear prediction problems in stock markets. This study harnes...
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Smart cities that use technology and data for efficiency optimization, sustainability, and well-being of citizens face a lot of challenges. Because all of the aforementioned challenges share a common characteristic of...
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Smart cities that use technology and data for efficiency optimization, sustainability, and well-being of citizens face a lot of challenges. Because all of the aforementioned challenges share a common characteristic of complexity, achieving success will need careful preparation and coordinated effort. This research presents a novel approach utilizing deep learning models to address issues about road congestion, specifically by offering secure routes for pedestrians and cyclists. The Global Positioning System (GPS) data stored in the cloud is used as input for the proposed work. In the proposed work, the flow of vehicles, their speed, and the occupancy have been predicted. The need for deep learning to resolve the traffic problem is that deep learning methods are highly efficient when compared to statistical techniques as they provide more than 90% of accuracy in forecasting. The novel approaches used in this paper are integrated Recurring Neural Networks (RNN)-Long Short Term Memory (LSTM)- Convolutional Neural Networks (CNN) to form RLC (RNN-LSTM-CNN) models. The system encompasses appropriate methods for improving the transportation system’s efficiency by mitigating environmental impacts. The implementation of Recurrent Neural Networks (RNN) along with Long Short-Term Memory (LSTM) are used to analyze historical traffic flow data by predicting future traffic conditions by optimizing traffic signal timings, traffic flow, and public transportation schedules to reduce idling time and fuel consumption, leading to lower emissions by predicting Electric Vehicle (EV) charging demand patterns, optimize charging stations’ locations and driver routes, and manage energy distribution more efficiently. The proposed Deep Learning-based models perform better when compared to the other methods and hold the potential to transform urban mobility, making it more efficient, safer, and environmentally friendly in the smart cities of the future as it provides higher forecasting accuracy
Due to the built-in light source within the endoscope, the illumination of bodily mucous can cause the formation of highlight regions due to reflection. This not only interferes with the diagnosis conducted by doctors...
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Modeling the trust of peers in peer-to-peer networks is pivotal in maintaining the security and functionality of the network. This trust is commonly perceived as a peer's reliability based on past interactions and...
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The Capacitated vehicle routing problem (CVRP) is a complex transportation issue that involves designing a set of routes for a group of homogenous vehicles to serve a set of customers at shortest distance travelled. I...
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A cashless society is becoming more and more of a reality because of the quick development of technology. This has made it easier for fraudsters to commit their crimes, to a certain extent. To solve this issue, fraud ...
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With the increasing demand for high-quality video streaming services, there is a need to enhance the Quality of Experience (QoE) for users, especially in environments with variable network conditions. This research pa...
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We prove that an Ε-approximate fixpoint of a map f : [0, 1]d → [0, 1]d can be found with O(d2(log 1Ε + log 1−1λ)) queries to f if f is λ-contracting with respect to an p-metric for some p ∈ [1, ∞) ∪ {∞}. This...
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