RETRACTED ARTICLE: Internet of Thing based Koch Fractal Curve Fractal Antennas for Wireless Applications

Yadav, Kusum; Jain, Anurag; Ahmed, Nada; Hamad, Sawsan Ali Saad; Dhiman, Gaurav; Alotaibi, Shoayee Dlaim

doi:10.1080/03772063.2022.2058631

Cited by 13 publications

(9 citation statements)

References 10 publications

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“…Although the above models have achieved improvement in the accuracy of summary generation, the recurrent neural network and its variants are all time-step-based sequence structures, which seriously hinders the parallel training of the model [16][17][18], resulting in the inference process being limited by memory, resulting in reduced encoding and decoding speed of the summary generation model, and increased training overhead [19][20][21][22][23]. On the other hand, the above works optimize the model to maximize the ROUGE index or maximum likelihood without considering the coherence or fluency of the summary sentence [24][25][26] and relying on the ground-truth value of the annotated summary text in advance. With supervised training, the data cost involved in model training is high.…”

Section: Related Workmentioning

confidence: 99%

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Abdeljaber

Ahmad

Alharbi

et al. 2022

Security and Communication Networks

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The purpose of automatic text summarising technology is to condense a given text while properly portraying the main information in the original text in a summary. To present generative text summarising approaches, on the other hand, restructure the original language and introduce new words when constructing summary sentences, which can easily lead to incoherence and poor readability. This research proposes a XAI (explainable artificial intelligence)-based Reinforcement Learning-based Text Summarization of Social IoT-Based Content using Reinforcement Learning. Furthermore, standard supervised training based on labelled data to improve the coherence of summary sentences has substantial data costs, which restricts practical applications. In order to do this, a ground-truth-dependent text summarization (generation) model (XAI-RL) is presented for coherence augmentation. On the one hand, based on the encoding result of the original text, a sentence extraction identifier is generated, and the screening process of the vital information of the original text is described. Following the establishment of the overall benefits of the two types of abstract writings, the self-judgment approach gradient assists the model in learning crucial sentence selection and decoding the selected key phrases, resulting in a summary text with high sentence coherence and good content quality. Experiments show that the proposed model's summary content index surpasses text summarising ways overall, even when there is no pre-annotated summary ground-truth; information redundancy, lexical originality, and abstract perplexity also outperform the current methods.

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Section: Related Workmentioning

confidence: 99%

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Abdeljaber

Ahmad

Alharbi

et al. 2022

Security and Communication Networks

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“…As this technique supports “Wiener Filter” that has the ability to observe the noisy images, here the objective function must be required. Now, the objective function as per FCM is [ 28 , 29 ]

where G pq = fuzzy factor and it is defined as

where x p , x r = pixels d pr = spatial Euclidian distance between x p and x r N p = set of neighbors σ qp = Fuzzy membership of p th pixel with respect to q σ qr = neighbor of σ qp N = total number of pixels in image C = cluster centre V = fuzziness of significant division …”

Section: Methodsmentioning

confidence: 99%

“…As this technique supports "Wiener Filter" that has the ability to observe the noisy images, here the objective function must be required. Now, the objective function as per FCM is [28,29]…”

Section: Proposed Ifrfcm For Image Segmentationmentioning

confidence: 99%

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

Mohapatra

Sahu

Almotiri

et al. 2022

Computational Intelligence and Neuroscience

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Nowadays, so many people are living in world. If so many people are living, then the diseases are also increasing day by day due to adulterated and chemical content food. The people may suffer either from a small disease such as cold and cough or from a big disease such as cancer. In this work, we have discussed on the encephalon tumor or cancer which is a big problem nowadays. If we will consider about the whole world, then there are deficiency of clinical experts or doctors as compared to the encephalon tumor affected person. So, here, we have used an automatic classification of tumor by the help of particle swarm optimization (PSO)-based extreme learning machine (ELM) technique with the segmentation process by the help of improved fast and robust fuzzy C mean (IFRFCM) algorithm and most commonly feature reduction method used gray level co-occurrence matrix (GLCM) that may helpful to the clinical experts. Here, we have used the BraTs (“Multimodal Brain Tumor Segmentation Challenge 2020”) dataset for both the training and testing purpose. It has been monitored that our system has given better classification accuracy as an approximation of 99.47% which can be observed as a good outcome.

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“…The First, the author provided an arrival-service model for FoT data traffic based on multilayer waiting for lines with finite-size intervals. The proposed policy was compared to first-in-first-out and multi-priority-discipline queue strategies with the help of a complete study of wait times and gaps in wait times [23][24][25]. Machine learning and Clustering based various methods are used for the text analysis [26], internet of things [27][28][29][30][31][32][33], and disease detection [34].…”

Section: Related Workmentioning

confidence: 99%

A Fog-Cluster Based Load-Balancing Technique

et al. 2022

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The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

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RETRACTED ARTICLE: Internet of Thing based Koch Fractal Curve Fractal Antennas for Wireless Applications

Cited by 13 publications

References 10 publications

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique

A Fog-Cluster Based Load-Balancing Technique

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