Search
Search Results
-
DQMMBSC: design of an augmented deep Q-learning model for mining optimisation in IIoT via hybrid-bioinspired blockchain shards and contextual consensus
Single-chained blockchains are highly secure but cannot be scaled to larger IIoT (Internet of Industrial Things) network scenarios due to storage...
-
Anomaly analytics in data-driven machine learning applications
Machine learning is used widely to create a range of prediction or classification models. The quality of the machine learning (ML) models depends not...
-
Temporal analysis of computational economics: a topic modeling approach
This study offers a comprehensive investigation into the thematic evolution within computational economics over the past two decades, leveraging...
-
Data reduction in big data: a survey of methods, challenges and future directions
Data reduction plays a pivotal role in managing and analyzing big data, which is characterized by its volume, velocity, variety, veracity, value,...
-
A Meta-learner approach to multistep-ahead time series prediction
The utilization of machine learning has become ubiquitous in addressing contemporary challenges in data science. Moreover, there has been significant...
-
A robust hubness-based algorithm for image data stream classification
Image data stream classification is in high demand and can be used in various contexts, such as public security, medicine, and remote sensing....
-
Video anomaly localization using modified faster RCNN with soft NMS algorithm
Localization of anomalies in surveillance videos is a critical component of smart and intelligent surveillance systems. The goal of anomaly detection...
-
An efficient facial emotion recognition using convolutional neural network with local sorting binary pattern and whale optimization algorithm
Facial emotion recognition is one of the fields of machine learning and pattern recognition. Facial expression recognition is used in a variety of...
-
Storage of weights and retrieval method (SWARM) approach for neural networks hybridized with conformal prediction to construct the prediction intervals for energy system applications
The prediction intervals represent the uncertainty associated with the model-predicted responses that impacts the sequential decision-making...
-
Temporal analysis of topic modeling output by machine learning techniques
Topic modeling is widely recognized as one of the most effective and significant methods of unsupervised text analysis. This method facilitates...
-
A novel discrete slash family of distributions with application to epidemiology informatics data
This study puts forward a new class of discrete distribution that can be used by the epidemiologists and medical scientists to model data relating to...
-
A review of sentiment analysis: tasks, applications, and deep learning techniques
Sentiment analysis, a transformative force in natural language processing, revolutionizes diverse fields such as business, social media, healthcare,...
-
A systematic review and meta-data analysis of clinical data repositories in Africa and beyond: recent development, challenges, and future directions
A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy...
-
Lcp-mixer: a lightweight model based on concept-level perception for NLP
Transformer-based models excel in natural language processing tasks but demand extensive computational resources and memory. To address this...
-
A hybrid deep learning neural network for early plant disease diagnosis using a real-world Wheat–Barley vision dataset: challenges and solutions
Approximately 35% of India’s annual crop yield is lost due to plant diseases. Due to a lack of lab equipment and infrastructure, early diagnosis of...
-
Twin neural network improved k-nearest neighbor regression
Twin neural network regression is trained to predict the difference between the regression targets of two data points rather than the individual...
-
Implicitly adaptive optimal proposal in variational inference for Bayesian learning
Overdispersed black-box variational inference uses importance sampling to decrease the variance of the Monte Carlo gradient in variational inference....
-
Reviewing the effectiveness of lexicon-based techniques for sentiment analysis in massive open online courses
Massive open online courses (MOOCs) platforms now play a big part in online learning. These platforms typically solicit course-related feedback from...
-
Comparison of thresholds for a convolutional neural network classifying medical images
Our aim is to compare different thresholds for a convolutional neural network (CNN) designed for binary classification of medical images. We consider...
-
Learning optimal deep prototypes for video retrieval systems with hybrid SVM-softmax layer
The research focuses on optimizing training time for video retrieval by producing optimized prototypes for a hybrid SVM-softmax regression...
