Social data analytics has recently gained esteem in predicting the future outcomes of important events like major political elections and box-office movie revenues. Related actions such as tweeting, liking, and commenting can provide valuable insights about consumer’s attention to a product or service. Such an information venue presents an interesting opportunity to harness data from various social media outlets and generate specific predictions for public acceptance and valuation of new products and brands. This new technology based on gauging consumer interest via the analysis of social media content provides a new and vital tool to the sales team to predict sales numbers with a great deal of accuracy.
This use case summarizes findings of a health monitoring study using empirical vibration and temperature data to build a predictive maintenance model. Sensors are placed in four different positions on the housing surface of three running motors at different health stages to study the model performance and its robustness with respect to sensor mounting and various operating conditions. Tens of thousands of data segments were processed and used to extract features and build supervised and unsupervised classification algorithms. A feed forward Neural Network was deployed to classify signals (unseen before by the network) from these 3 motors. Preliminary results look promising with 99.2 % classification accuracy. It is also worth to note the algorithm robustness with respect to sensor mounting.
Pattern recognition is the process of recognizing patterns by using a Machine Learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. Pattern recognition is the ability to detect arrangements of characteristics or data that yield information about a given system or data set. Predictive analytics in data science work can make use of pattern recognition algorithms to isolate statistically probable movements of time series data into the future. In a technological context, a pattern might be recurring sequences of data over time that can be used to predict trends, particular configurations of features in images that identify objects, frequent combinations of words and phrases for natural language processing (NLP), or particular clusters of behaviour on a network that could indicate an attack — among almost endless other possibilities. In IT, pattern recognition is a branch of Machine Learning that emphasizes the recognition of data patterns or data regularities in a given scenario. Pattern recognition involves classification and cluster of patterns.
The current business model of pharmaceutical industry where a new drug may take a decade and Billions of dollars to develop is no longer viable in this digital era of big data and cloud computing. Giant IT companies such as Amazon and Google are leveraging their deep pockets and strong AI footprints to lower the entry barrier to this vital sector and render classical models of drug discovery and development obsolete.
Yai365.com is a platform for automation and traceability of the overall annotation process. The platform allows several working roles to collaborate using allocated workflows of data files to move through the process while being traced in every step. In Yai24.com there are four different working roles where each role has specific access and supervision rights to manage the progress under its scope.
Speaker Diarization is the process of automatically annotate an audio stream with speakers’ labels. Generally, it is a task of determining the number of speakers who are active and their utterance duration in an audio file.
Audio Segmentation is a very important processing stage for most of audio analysis applications. The goal is to split an uninterrupted audio signal into homogeneous segments. Each segment should consist of a single sound that is acoustically different from other parts of the audio file. An accurate segmentation process can identify appropriate boundaries for partitioning given audio into homogeneous regions.
A cryptocurrency is a digital currency that is self-organized, whose value is determined mainly by social consensus. In addition to being decentralized, a cryptocurrency is not backed by any third party or central bank. Essentially, cryptocurrencies are limited entries in a database, not controlled by anyone but by the network itself, which is unchangeable unless specific conditions are fulfilled. All these conditions make cryptocurrency prices unpredictable and constantly fluctuating.