The convergence of the IoT and the Cloud makes our lives better by allowing for frictionless communication between humans and inanimate objects. Projecting analytics in the medical arena may help transform a sensitive...
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Effective network communication is essential in the current digital era, and cloud computing (CC) and the Internet of Things (IoT) are significant aspects of daily life. Accelerating and lowering the latency of data t...
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Financial fraud is a rising problem that affects both organizations and people, necessitating cutting-edge solutions to lessen its effects. The majority of machine learning models used now in the field of fraud detect...
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As social media continues to gain popularity, offensive language on the Internet is on the rise, and how to effectively detect it has become a hot spot in the academic community. Due to the strong context-dependence o...
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The wide use of Internet brought many challenges were security has become a need to protect information and resources against attacks such as Man-in-the-Middle (MITM). This type of attack aims to eavesdrop a communica...
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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
A novel synthesis method for wideband bandpass filter (BPF) with two in-band conjugate complex transmission zeros is proposed for realizing frequency- and attenuation-reconfigurable in-band notch. A new characteristic...
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In software development, system integrity is a measure of the impact code changes have on them. It is determined by the team's comprehension. However, rapid evolution of change commits and interaction in complex c...
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Optimizing the accuracy of SVM classification is one of the most important things in the world of machine learning. This study will use the SVM (Support Vector Machine) algorithm in classifying. This study aims to imp...
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