Sampling-based visual assessment computing techniques for an efficient social data clustering

Basha, M. S. Saleem; Mouleeswaran, S. K.; Prasad, Kavirayani R.

doi:10.1007/s11227-021-03618-6

Cited by 18 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advances in sampling are integrated with the decision probabilistic measures, scalable pruning, and initial data pre-processing [8]. Authors in [9] proposed another classifier schema "JOUS-Boost" for an effective solution to handle the learning of imbalanced data; which uses the combined learning techniques with the integration of adaptive boosting with jittering sampling techniques [10], [11].…”

Section: Dataset Apply Mdfs Sample Datamentioning

confidence: 99%

Untitled

2022

INDJCSE

View full text Add to dashboard Cite

Finding faults in software modules is an emerging issue in software reliability systems and the assessment of the fault is performed by software fault prediction systems (SFPS). An identification process of faultprone of software modules is the most prioritized before initiating the testing process of the same modules. The SFPS helps to improve the quality of software within the specified time and cost values. Early fault prediction in SFPS for the different software components showed significant results concerning the cost and time parameters. According to the state-of-the-art of SFPS, ensemble-based classifiers were performed as the best and most cost-effective when compared to other classifier methods. An ensemble random forest with adaptive synthetic sampling (E-RF-ADASYN) is developed recently is tested on a sample of PROMISE datasets and shows the cost-effective classifier results. Our proposed work is focused on the development of another sampling method, say, Multi-Distinguished-Features Sampling (MDFS) for obtaining the best sample illustration for representing the entire dataset. The experiments are conducted on bench-marked PROMISE datasets for demonstrating the efficiency of the proposed MDFS-E-RF compared to other traditional methods.

show abstract

Section: Dataset Apply Mdfs Sample Datamentioning

confidence: 99%

Untitled

2022

INDJCSE

View full text Add to dashboard Cite

show abstract

“…This is then defined as the scientific and technological paper text vector. The text similarity matrix between the scientific and technological papers is created by calculating the cosine similarity between the generated vectors to improve text clustering performance [45].…”

Section: B Scientific Paper's Each Data Vectorization and Matrix Crea...mentioning

confidence: 99%

Development of an Embedding Framework for Clustering Scientific Papers

Kim

Lee²,

Yoon³

2022

IEEE Access

View full text Add to dashboard Cite

In this era, research and development are becoming a continuous and accelerating process because technology changes rapidly with a short lifecycle. As a result, various methodologies are being developed to monitor these rapidly changing research trends; In particular, clustering method-related studies in science and technology documents are being developed with a variety of approaches. However, previous studies on document clustering methods focus on a specific field or language but do not take into consideration certain important pieces of information in science and technology documents. Therefore, this study proposes an embedding methodology that uses important content from scientific and technical documents. We took into consideration the importance of information containing core structures in science and technology documents and proposed a clustering methodology that analyzes structured and unstructured data, such as textual information, author information, and citation information. The proposed method combines both textual and structural data from the paper, using a method that focuses on screening important information by sections in science and technology documents. Then, Girvan-Newman clustering and Louvain clustering models are applied to generate embedding vectors and show evaluation results through the clustering indices. As a practical example, we applied the proposed methodology using paper data from the field of hydrogen cell vehicles. The results of this study will be effective in identifying gaps in technology for new technological development, identifying technology trends, and presenting directional information for future technology development.INDEX TERMS Clustering method, data mining, text mining, text analysis, scientific publishing, fuel cells.

show abstract

“…The GMM mean super vectors, however, have a high dimension [10]. In recent years, potential progress [11], [12] in text-independent speaker verification significantly influenced speaker recognition and clustering systems. Speaker recognition plays a role as biometric authentication [13], and it will help verify the speaker's identity through the speech of the respective speaker.…”

Section: Introductionmentioning

confidence: 99%

“…Find the acoustic speaker features [3] and assessment of the speaker's data clusters [4] [11]. Related speech research areas, speech clustering [8], speech recognition [5], and speech synthesizing [6] commonly face challenging problems from the unlabelled speaker data.…”

Section: Introductionmentioning

confidence: 99%

Sampling Based Fuzzy Speech Clustering Systems for Faster Communication with Virtual Robotics Towards Social Applications

Prasad¹,

Narasimhulu²,

KUMAR³

et al. 2023

Preprint

View full text Add to dashboard Cite

For social applications, virtual robotics technologies require the Internet of Things (IoT) and cloud services. IoT based speaker identification is required for virtual social robotics to operate in the most secure manner possible. Moreover, humans communicate using only natural speech to virtual robotics through IoT devices; Virtual robotics are the most emerging and which are required developing the speech-based identification models in the virtual cloud platforms. State-of-the-art of speech models defines the composite technique with the combination of k-means clustering and Gaussian mixture model (GMM) for speaker identification problems. Every speaker has their own annotation, acoustic, and utterance. The Gaussian mixture model (GMM) describes the modelling of features in the speech clustering system for speaker identification applications. These traditional systems are expensive for speech clustering of large datasets when considering the time and space parameters. Sampling based fuzzy-spherical k-means and sampling based fuzzy-mini-batch k-means(mbkm) are proposed to overcome the problem of scalability issue in the implementation of speech based virtual cloud based robotic systems. For demonstrating the effectiveness of the proposed fuzzy based speaker models, extensive experimental research is conducted using a variety of benchmark speech datasets.

show abstract

Sampling-based visual assessment computing techniques for an efficient social data clustering

Cited by 18 publications

References 21 publications

Untitled

Untitled

Development of an Embedding Framework for Clustering Scientific Papers

Sampling Based Fuzzy Speech Clustering Systems for Faster Communication with Virtual Robotics Towards Social Applications

Contact Info

Product

Resources

About