The Intelligent Internet of Things(IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approache...
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The Intelligent Internet of Things(IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence(AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most state-of-the-art research focuses independently on either data science or IIoT, rather than exploring their integration. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions.
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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Coping with noise in computing is an important problem to consider in large systems. With applications in fault tolerance (Hastad et al., 1987;Pease et al., 1980;Pippenger et al., 1991), noisy sorting (Shah and Wainwr...
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Coping with noise in computing is an important problem to consider in large systems. With applications in fault tolerance (Hastad et al., 1987;Pease et al., 1980;Pippenger et al., 1991), noisy sorting (Shah and Wainwright, 2018;Agarwal et al., 2017;Falahatgar et al., 2017;Heckel et al., 2019;Wang et al., 2024a;Gu and Xu, 2023;Kunisky et al., 2024), noisy searching (Berlekamp, 1964;Horstein, 1963;Burnashev and Zigangirov, 1974;Pelc, 1989;Karp and Kleinberg, 2007), among many others, the goal is to devise algorithms with the minimum number of queries that are robust enough to detect and correct the errors that can happen during the computation. In this work, we consider the noisy computing of the threshold-k function. For n Boolean variables x = (x1, ..., xn) ∈ {0, 1}n, the threshold-k function THk(·) computes whether the number of 1's in x is at least k or not, i.e., (Equation presented) The noisy queries correspond to noisy readings of the bits, where at each time step, the agent queries one of the bits, and with probability p, the wrong value of the bit is returned. It is assumed that the constant p ∈ (0, 1/2) is known to the agent. Our goal is to characterize the optimal query complexity for computing the THk function with error probability at most δ. This model for noisy computation of the THk function has been studied by Feige et al. (1994), where the order of the optimal query complexity is established;however, the exact tight characterization of the optimal number of queries is still open. In this paper, our main contribution is tightening this gap by providing new upper and lower bounds for the computation of the THk function, which simultaneously improve the existing upper and lower bounds. The main result of this paper can be stated as follows: for any 1 ≤ k ≤ n, there exists an algorithm that computes the THk function with an error probability at most δ = o(1), and the algorithm uses at most (Equation presented) queries in expectation. Here we define m (Eq
The earthquake early warning (EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is...
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The earthquake early warning (EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific information. In Japan's earthquake magnitude dataset, there is a chance of a high imbalance concerning the earthquakes above strong impact. This imbalance causes a high prediction error while training advanced machine learning or deep learning models. In this work, Conditional Tabular Generative Adversarial Networks (CTGAN), a deep machine learning tool, is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information. The result obtained using actual and mixed (synthetic and actual) datasets will be used for training the stacked ensemble magnitude prediction model, MagPred, designed specifically for this study. There are 13295, 3989, and 1710 records designated for training, testing, and validation. The mean absolute error of the test dataset for single station magnitude detection using early three, four, and five seconds of P wave are 0.41, 0.40, and 0.38 MJMA. The study demonstrates that the Generative Adversarial Networks (GANs) can provide a good result for single-station magnitude prediction. The study can be effective where less seismic data is available. The study shows that the machine learning method yields better magnitude detection results compared with the several regression models. The multi-station magnitude prediction study has been conducted on prominent Osaka, Off Fukushima, and Kumamoto earthquakes. Furthermore, to validate the performance of the model, an inter-region study has been performed on the earthquakes of the India or Nepal region. The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods. This has a high potential
Battery Energy Storage Systems (BESS) are critical for addressing the intermittent nature of Distributed Energy Resources (DERs) in power distribution networks. By enabling real-time monitoring and remote control, Int...
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Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most ...
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Synthetic data generation via generative artificial intelligence (GenAI) is essential for enhancing cybersecurity and safeguarding privacy in the Internet of Medical Things (IoMT) and healthcare. We introduce multifea...
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Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encounte...
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Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.
AI and reinforcement learning (RL) have attracted great attention in the study of multiplayer systems over the past decade. Despite the advances, most of the studies are focused on synchronized decision-making to atta...
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Brain tumors are ranked highly among the leading causes of cancer-related fatalities. Precise segmentation and quantitative assessment of brain tumors are crucial for effective diagnosis and treatment planning. Howeve...
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