Today, Machine Learning (a major application of Artificial Intelligence) is that one buzzword taking the technology industry by storm. With the breakthrough in computing technologies, Machine Learning has experienced tremendous growth in recent years. With the ever-growing demand for Machine Learning (ML), research in this field is gaining velocity to explore the unexplored components in ML.
The critical & foremost element of the research process is the selection of researchable, flexible and unique topic. Choosing such a research theme from a pool of most talked topics requires adept knowledge about the latest trends in the Machine Learning sector.
Some of the Machine Learning research topics that delivered big-results include:
1. Assessment of image quality
Theme – With images & videos being omnipresent, bits representing visual signals are experiencing growth like never before. Nevertheless, with the reducing bit rate, distortions are being introduced into the transmitted signals. To automatically analyze and optimize the performance of the transmission system, it is a must to have a metric for video and image quality. Since human beings are the receiver of visual signals, the metric chosen must pertain to the visual perception of the individuals in order to determine the visual distortion.
Solution – To assess the quality of the pictures/videos, a deep convolutional neural networks for full or no-reference image quality was developed. This enabled combined learning of spatial attention and local quality in a unified frame.
2. ML & communication
Theme – Communication systems produce an enormous amount of traffic data. These data can improve the design as well as the management of communication and network elements when collaborated with advanced ML approaches.
Solution – The research involved the development of video analysis algorithms operating with data encoded with block coding modes, transform coefficients and motion vectors of motion-compensated prediction residuals. As compressed domain methods avoid complete decoding of video, the processing can be done efficiently at lower costs.
3. Evaluation of biomedical data
Theme – Electroencephalography is an approach widely utilized for the acquisition of neural data in Brain-Computer Interfacing (BCI). BCI provides a broader scope for defining, monitoring and decoding human mental health. Mental states are reflectances of decision making and perception, making BCI an ideal element to offer into neutral processing.
Solution – A part of the research was concerned with developing robust techniques for assessing noisy, high-dimensional and non-stationary signals. The robust divergence-based methods for parameter estimation were investigated and EEG based technology for deriving brain correlation was developed.
4. Compression of neural networks
Theme – Large ML models comprising of deep neural networks or support vector machines are regarded as the most reliable prediction tools. The deep neural network possesses weight parameters whereas support vector machines use support vectors. The emergence of application on embedded system laid the foundation to process that determines if the Machine Learning technology can be reduced or not.
Solution – The research focused on the development of algorithms and theories for reducing learning machines that paves the way for practical applications in reduced ML areas. The solution includes the development of techniques to increase execution efficiency and reducing the complexity of deep neural networks.
5. Interpretable Machine Learning
Theme – Powerful Machine Learning including deep neural network, are capable of accumulating huge amount of training data. Due to the hardships faced in interpreting the results of the interface, deep neural networks are commonly regarded as block box methods by the research community. Due to its lack of transparency, the study on explainable artificial intelligence (XAI) has gained importance in recent years.
Solution – Visualising, explaining and interpreting deep neural networks and similar black-box ML models were developed. This was followed by developing a principled method to decompose the classification decision of deep neural networks. This method leverages on the structure of a neural network and was created on the basis of the layer-wise conversation principles.
6. Deep learning
Theme – Deep neural networks are successful in recent times due to their efficiency in the internal representation of a learning problem which is accomplished by applying a nonlinear transformation to the input. This not only allows us to study the optimal features of a given problem but also enables us to handle the issues with dimensionality.
Solution – Deep architecture for different classification and recognition tasks were explored. The recognition tasks included image classification, document topic classification, virtual question answering, sentiment analysis, and many more. The research focused on interpreting the reasoning of the learning system and theoretical assessment of deep neural networks.
These are a few Machine Learning research topics that made a significant contribution to the technology sector. Explore the less talked topics in this field and add value to your existing knowledge base.