Extensive research has been conducted for the computational analysis of mass spectrometry based proteomics data, however most of the traditional computational approaches take the assumption that the acquired spectra are generated from the fragmentation of a single precursor and the peptide is simply a linear sequence of amino acid residues. This ubiquitous assumption is impeding the utility of those computational approaches, especially when handling those non-canonical tandem mass spectra.
Intrusion detection has attracted the attention of many researchers in identifying the ever-increasing issue of intrusive activities. In particular, anomaly detection has been the main focus of many researchers due to its potential in detecting novel attacks. However, its adoption to real-world applications has been hampered due to system complexity as these systems require a substantial amount of testing, evaluation, and tuning prior to deployment.
Often, a single employment notice may receive hundreds of applications. Manual inspection of applications is extremely time-consuming, and may be approximated by a computer program. Such a program would automatically extract a number of features from each application. For example, relevant work experience, skills, and qualifications might represent appropriate features. After extracting these features, the system would be able to score and rank applications in an effort to reduce the number of applications that would then need to be reviewed.
Maritime situation analysis is critical for dynamic decision-making in responding to real-world situations. Rapidly unfolding situations that pose an imminent danger or threat to critical infrastructure or public safety require interactive decision-making to enable a swift response. The main objective of this project is to design a robust methodical framework for the development of intelligent systems and services for real-time anomaly detection in marine traffic, applied to large volume maritime surveillance operations.
Automatic harvesting of mushroom produce is a promising opportunity for mushroom growers to increase their revenues. This increased revenue will be obtained through savings in human labour as well as the expected increase in yield and quality due to the consistency of automated solutions compared to manual ones. Automated harvesting solutions do already exist for many other crops such as apple, lime and tomatoes. However, the development of an automated harvesting solution for mushroom is much more challenging in which a commercial system still does not exist.
With more and more buildings being controlled by automation systems, one would expect their energy performance to be optimised. This is not the case however. Buildings can still go out of tune, and building operators can become overwhelmed by the alarms sounding from the automation systems, not knowing how to prioritize them. SES consulting is well poised to provide a human in the loop performance analysis service, leveraging their expert knowledge and the data from the building automation system.
Ethernet networks are typically best effort networks where traffic flows may contribute on creating network congestion and lead the switches to start dropping packets randomly. This results in unstable network latency that some applications cannot tolerate, especially in the context of 5G networks where delay constraints are very tight.
Solar cells which convert solar energy directly into electricity are among one of the most viable solutions to the worlds foreseeable energy crisis and global environmental issues. One key strategy to improve the efficiency of solar cells is to enhance the overlap between their absorption spectra and the solar spectrum. When two or more subcells with distinct and complementary absorption spectra are stacked, the tandem solar cells are created and a broader range of the solar spectrum can be absorbed and more solar energy can be harvested.
Frequent usage patterns generated can provide valuable information for several applications such as platform restructuring and recommendation. In this project, we aim to compare different practical methods, and to investigate the effect of user identity and user intention information on them. To that end, a technique and a framework need to be developed, in which frequent patterns are composed of more refined analysis result instead of simple frequent sequences of basic operations over all users behavior.
I am to import ten years worth of amassed historical data on news events, price movement of equities and public sentiment metrics to Microsoft Azure platform for study and analysis through the latest Data Mining techniques with an Economics point of view to uncover the hidden correlation and casualty between events and price movement of global markets in multiple timeframes (three hours, daily, weekly, monthly and yearly).