Interoperability and Data Storage in Internet of Multimedia Things: Investigating Current Trends, Research Challenges and Future Directions

Yang, Guojie; Jan, Mian Ahmad; Rehman, Ateeq Ur; Babar, Muhammad; Aimal, M. Muhammad; Verma, Sahil

doi:10.1109/access.2020.3006036

Cited by 36 publications

(13 citation statements)

References 103 publications

(101 reference statements)

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“…To meet the stringent requirements, the underlying protocol is required for providing [23] interoperable and seamless communication in IoMT. It is a challenging problem that interoperability of multimedia sensors due to the heterogeneous nature.…”

Section: Literature Surveymentioning

confidence: 99%

Energy Efficient Unequal Fault Tolerance Clustering Approach

Ramisetty¹,

Anand²,

Kavita³

et al. 2023

Computer Systems Science and Engineering

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For achieving Energy-Efficiency in wireless sensor networks (WSNs), different schemes have been proposed which focuses only on reducing the energy consumption. A shortest path determines for the Base Station (BS), but fault tolerance and energy balancing gives equal importance for improving the network lifetime. For saving energy in WSNs, clustering is considered as one of the effective methods for Wireless Sensor Networks. Because of the excessive overload, more energy consumed by cluster heads (CHs) in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure. For increasing the WSNs' lifetime, the CHs selection has played a key role in energy consumption for sensor nodes. An Energy Efficient Unequal Fault Tolerant Clustering Approach (EEUFTC) is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization (PSO). In this approach, an optimal Master Cluster Head (MCH)-Master data Aggregator (MDA), selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator (MDA), and Master Cluster Head. The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS. Thus, the MCH overhead reduces. During the heavy communication of data, overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA). To describe the proposed method superiority based on various performance metrics, simulation and results are compared to the existing methods.

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Section: Literature Surveymentioning

confidence: 99%

Energy Efficient Unequal Fault Tolerance Clustering Approach

Ramisetty¹,

Anand²,

Kavita³

et al. 2023

Computer Systems Science and Engineering

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“…As far as the healthcare industry is concerned, it is using quantum computations to carry a patient-centric concern for medical care clients. Quantum computing and healthcare systems enable hardware solutions that can significantly benefit the healthcare industry in evaluating and treating complicated medical situations [36]. Quantum computing imbues the digital world with quantum physics, enabling computers to process data quicker and solve more complex issues [37].…”

Section: Role and Platforms Of Quantum-enhanced Machinementioning

confidence: 99%

“…At the end, there is Naïve Baye's classifier which is a set of classifier algorithm that relies on Baye's theorem. It is used to calculate the conditional probability of a hypothesis being true given that order of information is also true [36]. Equation ( 9) is as follows…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

Kumar

Koul

Sisodia

et al. 2021

Wireless Communications and Mobile Computing

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Quantum-enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient-oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum-enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min-max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum-enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F 1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum-enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum-enhanced machine learning algorithms to select the optimal method for detecting heart failure.

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“…News is widely available in electronic format on the World Wide Web these days, and it has proven to be a valuable data source [ 11 ]. The volume of news, on the other hand, is enormous, and it is unclear how to use it most efficiently for domain-specific research.…”

Section: Introductionmentioning

confidence: 99%

A Complete Process of Text Classification System Using State-of-the-Art NLP Models

Dogra

Verma

Kavita

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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With the rapid advancement of information technology, online information has been exponentially growing day by day, especially in the form of text documents such as news events, company reports, reviews on products, stocks-related reports, medical reports, tweets, and so on. Due to this, online monitoring and text mining has become a prominent task. During the past decade, significant efforts have been made on mining text documents using machine and deep learning models such as supervised, semisupervised, and unsupervised. Our area of the discussion covers state-of-the-art learning models for text mining or solving various challenging NLP (natural language processing) problems using the classification of texts. This paper summarizes several machine learning and deep learning algorithms used in text classification with their advantages and shortcomings. This paper would also help the readers understand various subtasks, along with old and recent literature, required during the process of text classification. We believe that readers would be able to find scope for further improvements in the area of text classification or to propose new techniques of text classification applicable in any domain of their interest.

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Interoperability and Data Storage in Internet of Multimedia Things: Investigating Current Trends, Research Challenges and Future Directions

Cited by 36 publications

References 103 publications

Energy Efficient Unequal Fault Tolerance Clustering Approach

Energy Efficient Unequal Fault Tolerance Clustering Approach

Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things

A Complete Process of Text Classification System Using State-of-the-Art NLP Models

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