Microblog Emotion Analysis Method Using Deep Learning in Spark Big Data Environment

Jun-ya, Yan; Ma, Xiaohui

doi:10.1155/2022/1909312

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…In this work, the presented CEOGC-DLLP technique follows the DBN model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication [25]. The DBN model training can be classified as follows: unsupervised pre-training procedure depends on RBM and supervised parameter adjustment technique.…”

Section: B Load Prediction Using Dbn Modelmentioning

confidence: 99%

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Aljebreen,

Obayya,

Mahgoub

et al. 2024

IEEE Access

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Currently, there is an emerging requirement for applications related to the Internet of Things (IoT). Though the capability of IoT applications is huge, there are frequent limitations namely energy optimization, heterogeneity of devices, memory, security, privacy, and load balancing (LB) that should be solved. Such constraints must be optimised to enhance the network's efficiency. Hence, the core objective of this study was to formulate the intelligent-related cluster head (CH) selection method to establish green communication in IoT. Therefore, this study develops a chaotic equilibrium optimizer-based green communication with deep learning-enabled load prediction (CEOGC-DLLP) in the IoT environment. The study recognizes the emerging need for IoT applications and acknowledges the critical challenges, such as energy optimization, device heterogeneity, memory constraints, security, privacy, and load balancing, which are essential to enhancing the efficiency of IoT networks. The presented CEOGC-DLLP technique mainly accomplishes green communication via clustering and future load prediction processes. To do so, the presented CEOGC-DLLP model derives the CEOGC technique with a fitness function encompassing multiple parameters. In addition, the presented CEOGC-DLLP technique follows the deep belief network (DBN) model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication. The experimental assessment of the CEOGC-DLLP technique is performed and the outcomes are investigated under different aspects. The comparison study represents the supremacy of the CEOGC-DLLP method compared to existing techniques with a maximum throughput of 64662 packets and minimum MSE of 0.2956.

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Section: B Load Prediction Using Dbn Modelmentioning

confidence: 99%

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Aljebreen,

Obayya,

Mahgoub

et al. 2024

IEEE Access

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“…Apache spark is a data processing framework [18] that can execute fast processing jobs on very big data and can be distributed across multiple computers either alone or in combination with other distributed computing tools [19]. These two key attributes are relevant to the fields of big data and machine learning, which require the integration of enormous computing resources to process huge data.…”

Section: Apache Sparksmentioning

confidence: 99%

“…The framework of Spark is designed explicitly to collaborate and run big data analysis for real-time applications [16,18,21]. It became a solid establishment for data learning and took the field of big data analytics at high speed.…”

Section: Apache Sparksmentioning

confidence: 99%

Scheduling of Jobs Allocation for Apache Spark Using Kubernetes for Efficient Execution of Big Data Application

2023

IJIES

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The use of cloud services is in high demand due to their high storage and computing capacity. Apache spark provides an open deployment framework for data storage and computation using cluster computing. The specified spark core scheduler uses FIFO to manage job execution in batches. However, it may not be suitable for large-scale clusters due to the unevenness in managing resource allocation between different types of applications. Because of this, most of the work executors are still underutilized and resources are wasted in all the pods, leading to cost inefficiencies. Using apache spark on kubernetes to run cloud applications will ensure rapid resource management for workload execution. Incoming workloads vary widely across applications, so it is critical to manage workload allocation to ensure QoS and cost efficiency. This paper proposes a job scheduling mechanism (JSM) for apache spark on kubernetes to dynamically schedule job allocation for the efficient execution of various big data applications. The JSM process predicts the cluster load and suggests relocation of workloads to efficiently distribute the workload to the lower loaded pods in a standard cluster to optimize cost performance. It identifies the upcoming workload of a job and determines the best-fit pod and aims to reduce the usage of CPU and memory which result in enhancing cost efficiency. The JSM's effective management of job allocation and migration among the underload pods preserves the resource and enhances cost efficiency. Experimental settings are configured to evaluate cluster resources with benchmark statistics output for application job execution. The outcome results of cost, job performance, and scheduling overhead show improved cost efficiency for job execution. The comparison with the existing scheduler with varying request load shows an improvisation of 2% in cost efficiency and 3% lower scheduling overhead.

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“…This is because it addresses the gap that has been observed in the existing body of work. Through the provision of a refined and accurate system for the prediction of mood for election slogans, the suggested method intends to overcome the limitations that have been imposed by previous research (7) . It is important to note that our efforts are driven by the understanding that existing methodologies, despite the fact that they are promising, are not sufficient in providing a comprehensive answer to the problem of conducting sentiment research within the context of political campaigns.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Slogans with Predicted Sentiment from Twitter using a Novel Hybrid SDG-LSTM Model for Election Campaigns

Sangle,

Sedamkar

2024

IJST

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Objectives: The primary objectives of this study encompass the enhancement of election campaign strategies through the synthesis of sentiment-laden slogans derived from Twitter data. This is achieved by employing a novel Hybrid SDG-LSTM model, aiming to improve sentiment prediction accuracy and communication efficacy in the context of political campaigns. Methods: The process of slogan generation relies on sentiment prediction derived from sentiment-laden tweets. The proposed sentiment analysis methods for election campaign slogans encompass Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). A novel approach is introduced through the Hybrid SDG-LSTM model, leveraging the combination of Self-Distillation Guidance (SDG) with LSTM to enhance sentiment prediction accuracy and efficiency. This innovative method aims to provide a more robust and effective means of analyzing and generating slogans for election campaigns. Findings: The performance assessment of Deep Learning models, GRU, LSTM, and the Hybrid architecture, unveiled compelling outcomes. GRU showcased a commendable accuracy of 92.98%, while LSTM impressed with 95.91%. Remarkably, the Hybrid Spatial LSTM with GRU surpassed both, achieving perfection with 100% accuracy, precision, recall, and an exceptionally low loss of 0.0. These results underscore the superior performance and efficacy of the Hybrid model in sentiment analysis tasks. Novelty: The novelty of this research is encapsulated in the introduction of the Hybrid Spatial LSTM with GRU model, which demonstrates groundbreaking 100% accuracy, surpassing current models. This innovation capitalizes on the synergistic fusion of spatial attention mechanisms and the dynamic nature of GRU, marking a substantial advancement and establishing a new benchmark for highly accurate predictions in the domain of sentiment analysis. Keywords: Slogan Generation, Sentiment Analysis, Election Campaign, Deep Learning, LSTM, GRU

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Microblog Emotion Analysis Method Using Deep Learning in Spark Big Data Environment

Cited by 3 publications

References 26 publications

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Scheduling of Jobs Allocation for Apache Spark Using Kubernetes for Efficient Execution of Big Data Application

Synthesis of Slogans with Predicted Sentiment from Twitter using a Novel Hybrid SDG-LSTM Model for Election Campaigns

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