Feature Level Ensemble Method for Classifying Multi-Media Data

Multimedia data consists of several different types of data, such as numbers, text, images, audio etc. and they usually need to be fused or integrated before analysis. This study investigates a feature-level aggregation approach to combine multimedia datasets for building heterogeneous ensembles for classification. It firstly aggregates multimedia datasets at feature level to form a normalised big dataset, then uses some parts of it to generate classifiers with different learning algorithms. Finally, it applies three rules to select appropriate classifiers based on their accuracy and/or diversity to build heterogeneous ensembles. The method is tested on a multimedia dataset and the results show that the heterogeneous ensembles outperform the individual classifiers as well as homogeneous ensembles. However, it should be noted that, it is possible in some cases that the combined dataset does not produce better results than using single media data

Machine Learning Ensemble Methods for Classifying Multi-media Data

Multimedia data have, over recent years, been produced in many fields. They have important applications for such diverse areas as social media and healthcare, due to their capacity to capture rich information. However, their unstructured and separated nature gives rise to various problems. In particular, fusing and integrating multi-media datasets and finding effective ways to learn from them have proven to be major challenges for machine learning. In this thesis we investigated the development of the ensemble methods for classifying multi-media data in two key aspects: data fusion and model selection. For the data fusion, we devised two different strategies. The first one is the Feature Level Ensemble Method (FLEM) that aggregates all the features into a single dataset and then generates the models to build ensembles using this dataset. The second one is the Decision Level Ensemble Method (DLEM) that generates the models from each sub dataset individually and then aggregates their outputs with a decision fusion function. For the model selection we derived four different model selection rules. The first rule, R0, uses just the accuracy to select models. The rules R1 and R2 use firstly accuracy and then diversity to select models. In R3, we defined a generalised function that combines the accuracy and diversity with different weights to select models to build an ensemble. Our methods were compared with existing well known ensemble methods using the same dataset and another dataset that became available after our methods had been developed. The results were critically analysed and the statistical significance analyses of the results show that our methods had better performance in general and the generalised R3 is the most effective rule in building ensembles

Heterogeneous Ensemble for Imaginary Scene Classification

In data mining, identifying the best individual technique to achieve very reliable and accurate classification has always been considered as an important but non-trivial task. This paper presents a novel approach - heterogeneous ensemble technique, to avoid the task and also to increase the accuracy of classification. It combines the models that are generated by using methodologically different learning algorithms and selected with different rules of utilizing both accuracy of individual modules and also diversity among the models. The key strategy is to select the most accurate model among all the generated models as the core model, and then select a number of models that are more diverse from the most accurate model to build the heterogeneous ensemble. The framework of the proposed approach has been implemented and tested on a real-world data to classify imaginary scenes. The results show our approach outperforms other the state of the art methods, including Bayesian network, SVM an d AdaBoost

Decision level ensemble method for classifying multi-media data

In the digital era, the data, for a given analytical task, can be collected in different formats, such as text, images and audio etc. The data with multiple formats are called multimedia data. Integrating and fusing multimedia datasets has become a challenging task in machine learning and data mining. In this paper, we present heterogeneous ensemble method that combines multi-media datasets at the decision level. Our method consists of several components, including extracting the features from multimedia datasets that are not represented by features, modelling independently on each of multimedia datasets, selecting models based on their accuracy and diversity and building the ensemble at the decision level. Hence our method is called decision level ensemble method (DLEM). The method is tested on multimedia data and compared with other heterogeneous ensemble based methods. The results show that the DLEM outperformed these methods significantly

Multiple blockholders and their impacts on innovation, valuation and default risk

EMBARGOED - expected end date 15.04.2026</p

Stress Monitoring Using Machine Learning, IoT and Wearable Sensors

The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible. Stress monitoring with wearable sensors and the Internet of Things (IoT) is a potential application that can enhance wellness and preventative health management. Healthcare professionals have harnessed robust systems incorporating battery-based wearable technology and wireless communication channels to enable cost-effective healthcare monitoring for various medical conditions. Network-connected sensors, whether within living spaces or worn on the body, accumulate data crucial for evaluating patients' health. The integration of machine learning and cutting-edge technology has sparked research interest in addressing stress levels. Psychological stress significantly impacts a person's physiological parameters. Stress can have negative impacts over time, prompting sometimes costly therapies. Acute stress levels can even constitute a life-threatening risk, especially in people who have previously been diagnosed with borderline personality disorder or schizophrenia. To offer a proactive solution within the realm of smart healthcare, this article introduces a novel machine learning-based system termed "Stress-Track". The device is intended to track a person's stress levels by examining their body temperature, sweat, and motion rate during physical activity. The proposed model achieves an impressive accuracy rate of 99.5%, showcasing its potential impact on stress management and healthcare enhancement