Image blurring refers to the degradation of an image wherein the image's overall sharpness decreases. Image blurring is caused by several factors. Additionally, during the image acquisition process, noise may get ...
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Due to developments in technologies like Cloud Computing (CC), the Internet of Things (IoT), etc., the data volume transmitted across communication infrastructures has skyrocketed recently. In order to make network sy...
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ISBN:
(数字)9798350359299
ISBN:
(纸本)9798350359305
Due to developments in technologies like Cloud Computing (CC), the Internet of Things (IoT), etc., the data volume transmitted across communication infrastructures has skyrocketed recently. In order to make network systems susceptible, attackers have increased their determinations. Improving the security of such network systems is very crucial. Using Deep Learning (DL) methods with an Intrusion Detection System (IDS) framework is put into action in this research. The Gated Recurrent Unit (GRU) as well as Support Vector machine (SVm) are used in this study for attack detection. Therefore, the implementation of Softmax in the GRU model's last output layer, this study introduces a linear SVm. The NSL-KDD dataset is taken into account to evaluate the proposed IDS framework's performance. In addition, when the number of features increases, current IDSs have poor test accuracy ratings when it comes to identifying new attacks. Each dataset's feature space was reduced using an XGBoost (XGB)-based feature selection approach in this research. After that, 22 detailed characteristics were selected from the NSL-KDD dataset using XGB. The results show that, the proposed IDS paradigm outperformed other approaches.
monitoring water quality has become crucial to provide clean and safe water. The traditional monitoring procedures are useless because they take a lot of function, require a long time, and cannot give results instantl...
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The importance of sleep to a person's health and well-being cannot be overstated. Sleep monitoring and analysis control has been transformed by the combination of Internet of Things (IoT) technologies and wearable...
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Climate change has accelerated the dynamics of cryospheric and oceanographic systems, leading to rising sea levels, increased ice melt, and significant shifts in polar regions. monitoring these changes is crucial, yet...
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The novel coronavirus 2019 (COVID-2019), which initially proved its existence in Wuhan city of China in December 2019, spread quickly around the globe and turned into a pandemic. It has caused a staggering impact on a...
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To monitor a specified Google Cloud Storage bucket for any changes or accesses made to its contents. When an access or change is detected, the cloud function will automatically send an email notification to a specifie...
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machine learning technique which uses artificial neural networks to learn representations. Phishing is a form of fraud in which the attacker tries to learn credential information from the websites. Web phishing is to ...
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To improve text input for motor-disabled people, this research uses the Internet of Things (IoT) and machine learning. Swipe-to-Type, a popular touch-based input technique, is the study's focus. User swipe motions...
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Reinforcement Learning has work wonders in games like Atari and AlphaZero. Recent advancement in Deep Reinforcement Learning showcase it’s ability in the active Prosthesis as well. RL is being used widely to solve pr...
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Reinforcement Learning has work wonders in games like Atari and AlphaZero. Recent advancement in Deep Reinforcement Learning showcase it’s ability in the active Prosthesis as well. RL is being used widely to solve problems where Learning of the Agent in its own environment is as necessary as training the model beforehand. However, model developed, and successful in the gaming environment could still need to be tuned to be effective with Real Time devices such as Prosthetic Limb and other Real-World devices. In this article, main challenges are presented which we face while working on a model Based and model Free Reinforcement Learning in real world environment and suggesting an approach which would work uniformly on most of the Real Time scenarios. We observed the performance and noticed that there are couple of factors which needs to be taken care of in Real Time Applications which are not much though about in games and other online applications. We also compared the algorithms such as Policy Proximal Optimization (PPO) and Deep Deterministic Policy Gradient (DDPG) vs model Based Policy search with Gaussian Processes and found out that a mix of model-Based and model-Free (mBmF) performed the best individually despite of all the challenges.
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