An Efficient Topic Modeling Approach for Text Mining and Information Retrieval through K-means Clustering

Rashid, Junaid; Shah, Syed Muhammad Adnan; Irtaza, Aun

doi:10.22581/muet1982.2001.20

Cited by 15 publications

(7 citation statements)

References 25 publications

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“…This study showed that pre-processing of data prior to model building enhances prediction accuracy ( 35 ). Machine learning methods are also used for the detection of some other medical diseases detection ( 36 – 38 ), text mining ( 39 – 42 ) and network security ( 43 – 47 ). Another analysis is conducted to predict the risks associated with diabetes.…”

Section: Literature Reviewmentioning

confidence: 99%

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Rashid

Batool

Kim

et al. 2022

Front. Public Health

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Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.

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Section: Literature Reviewmentioning

confidence: 99%

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Rashid

Batool

Kim

et al. 2022

Front. Public Health

Self Cite

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“…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]. Under any given IoT workload, the mathematical model estimates the minimum number of fog nodes required to meet QoS requirements.…”

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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“…Khurana and Verma [25] introduced a new algorithm to solve high-dimensionality, named modified biogeographybased optimization, which uses a feature weighting algorithm to modify the ranking of variables. Rashid et al [26] proposed new k-means topic modeling to capture better semantic topics.…”

Section: Feature Selection For Text Classificationmentioning

confidence: 99%

Few-Shot Text Classification with Global–Local Feature Information

Wang

Cheng

et al. 2022

Sensors

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Meta-learning frameworks have been proposed to generalize machine learning models for domain adaptation without sufficient label data in computer vision. However, text classification with meta-learning is less investigated. In this paper, we propose SumFS to find global top-ranked sentences by extractive summary and improve the local vocabulary category features. The SumFS consists of three modules: (1) an unsupervised text summarizer that removes redundant information; (2) a weighting generator that associates feature words with attention scores to weight the lexical representations of words; (3) a regular meta-learning framework that trains with limited labeled data using a ridge regression classifier. In addition, a marine news dataset was established with limited label data. The performance of the algorithm was tested on THUCnews, Fudan, and marine news datasets. Experiments show that the SumFS can maintain or even improve accuracy while reducing input features. Moreover, the training time of each epoch is reduced by more than 50%.

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An Efficient Topic Modeling Approach for Text Mining and Information Retrieval through K-means Clustering

Cited by 15 publications

References 25 publications

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

A Fog-Cluster Based Load-Balancing Technique

Few-Shot Text Classification with Global–Local Feature Information

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