On predictability of rare events leveraging social media: a machine learning perspective

Information extracted from social media streams has been leveraged to forecast the outcome of a large number of real-world events, from political elections to stock market fluctuations. An increasing amount of studies demonstrates how the analysis of social media conversations provides cheap access to the wisdom of the crowd. However, extents and contexts in which such forecasting power can be effectively leveraged are still unverified at least in a systematic way. It is also unclear how social-media-based predictions compare to those based on alternative information sources. To address these issues, here we develop a machine learning framework that leverages social media streams to automatically identify and predict the outcomes of soccer matches. We focus in particular on matches in which at least one of the possible outcomes is deemed as highly unlikely by professional bookmakers. We argue that sport events offer a systematic approach for testing the predictive power of social media, and allow to compare such power against the rigorous baselines set by external sources. Despite such strict baselines, our framework yields above 8% marginal profit when used to inform simple betting strategies. The system is based on real-time sentiment analysis and exploits data collected immediately before the games, allowing for informed bets. We discuss the rationale behind our approach, describe the learning framework, its prediction performance and the return it provides as compared to a set of betting strategies. To test our framework we use both historical Twitter data from the 2014 FIFA World Cup games, and real-time Twitter data collected by monitoring the conversations about all soccer matches of four major European tournaments (FA Premier League, Serie A, La Liga, and Bundesliga), and the 2014 UEFA Champions League, during the period between Oct. 25th 2014 and Nov. 26th 2014.Comment: 10 pages, 10 tables, 8 figure

An Industrial Multi Agent System for real-time distributed collaborative prognostics

Despite increasing interest, real-time prognostics (failure prediction) is still not widespread in industry due to the di fficulties of existing systems to adapt to the dynamic and heterogeneous properties of real asset fleets. In order to address this, we present an Industrial Multi Agent System for real-time distributed collaborative prognostics. Our system fufil ls all six core properties of Advanced Multi Agent Systems: Distribution, Flexibility, Adaptability, Scalability, Leanness, and Resilience. Experimental examples of each are provided for the case of prognostics using the C-MAPPS engine degradation data set, and data from a fleet of industrial gas turbines. Prognostics are performed using the Weibull Time To Event - Recurrent Neural Network algorithm. Collaboration is achieved by sharing information between agents in the system. We conclude that distributed collaborative prognostics is especially pertinent for systems with presence of sensor faults, limited computing capabilities or significant fleet heterogeneity

Evaluation of the Liquisolid Compacts Using Response Surface Methodology

Liquisolid Compacts technique has potential to develop sustained release formulations. It involves conversion of liquid drug (either solution or suspension) in non-volatile solvent into free-flowing, non adherent, dry looking and readily compressible powder. In the present work, an attempt was made to develop such formulation of Diltiazem HCl and evaluation using Response surface methodology. Liquisolid compacts were prepared by dissolving Diltiazem HCl in Polyethylene Glycol 400. Then a binary mixture of carrier-coating material, Avicel and Aerosil, was added to liquid medication under continuous mixing in mortar. The HPMC K4M was used as adjuvant for sustaining the drug release.  The pre-compression studies for all the formulations were also carried out. The Liquisolid compacts were evaluated in-vitro dissolution studies. The experimental data was evaluated using Design Expert Software. The % Drug Concentration, ratio of Carrier to Coating material and amount of HPMC K4M are taken as three factors. Response Surface methodology was used to study the influence of the each factor on the response. The present investigation showed that Polyethylene Glycol 400 has important role in release retardation of drug in Liquisolid compacts. The reduction in Tg can be reason for same. The Response surface methodology showed that all the factors were significantly affect the release at 16 hrs.

Evaluation of the Liquisolid Compacts Using Response Surface Methodology

Liquisolid Compacts technique has potential to develop sustained release formulations. It involves conversion of liquid drug (either solution or suspension) in non-volatile solvent into free-flowing, non adherent, dry looking and readily compressible powder. In the present work, an attempt was made to develop such formulation of Diltiazem HCl and evaluation using Response surface methodology. Liquisolid compacts were prepared by dissolving Diltiazem HCl in Polyethylene Glycol 400. Then a binary mixture of carrier-coating material, Avicel and Aerosil, was added to liquid medication under continuous mixing in mortar. The HPMC K4M was used as adjuvant for sustaining the drug release.  The pre-compression studies for all the formulations were also carried out. The Liquisolid compacts were evaluated in-vitro dissolution studies. The experimental data was evaluated using Design Expert Software. The % Drug Concentration, ratio of Carrier to Coating material and amount of HPMC K4M are taken as three factors. Response Surface methodology was used to study the influence of the each factor on the response. The present investigation showed that Polyethylene Glycol 400 has important role in release retardation of drug in Liquisolid compacts. The reduction in Tg can be reason for same. The Response surface methodology showed that all the factors were significantly affect the release at 16 hrs.

Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System

Recent advancement in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. While accuracy is important, focusing primarily on it poses an overfitting danger, exposing manufacturers to risk, ultimately hindering the adoption of these techniques. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty in a cyber-physical manufacturing system (CPMS) scenario. Then, we propose a multi-agent system architecture which leverages probabilistic machine learning as a means of achieving such criteria. We propose possible scenarios for which our architecture is useful and discuss future work. Experimentally, we implement Bayesian Neural Networks for multi-tasks classification on a public dataset for the real-time condition monitoring of a hydraulic system and demonstrate the usefulness of the system by evaluating the probability of a prediction being accurate given its uncertainty. We deploy these models using our proposed agent-based framework and integrate web visualisation to demonstrate its real-time feasibility

An Industrial Multi Agent System for real-time distributed collaborative prognostics

Despite increasing interest, real-time prognostics (failure prediction) is still not widespread in industry due to the di fficulties of existing systems to adapt to the dynamic and heterogeneous properties of real asset fleets. In order to address this, we present an Industrial Multi Agent System for real-time distributed collaborative prognostics. Our system fufil ls all six core properties of Advanced Multi Agent Systems: Distribution, Flexibility, Adaptability, Scalability, Leanness, and Resilience. Experimental examples of each are provided for the case of prognostics using the C-MAPPS engine degradation data set, and data from a fleet of industrial gas turbines. Prognostics are performed using the Weibull Time To Event - Recurrent Neural Network algorithm. Collaboration is achieved by sharing information between agents in the system. We conclude that distributed collaborative prognostics is especially pertinent for systems with presence of sensor faults, limited computing capabilities or significant fleet heterogeneity

Recurrent Neural Networks for real-time distributed collaborative prognostics

We present the first steps towards real-time distributed collaborative prognostics enabled by an implementation of the Weibull Time To Event - Recurrent Neural Network (WTTE-RNN) algorithm. In our system, assets determine their time to failure (TTF) in real-time according to an asset-specific model that is obtained in collaboration with other similar assets in the asset fleet. The presented approach builds on the emergent field of similarity analysis in asset management, and extends it to distributed collaborative prognostics. We show how through collaboration between assets and distributed prognostics, competitive time to failure estimates can be obtained

Natural dye yielding plants

228-234Every herb can be used to make dye. Herbal dyes being natural tend to be softer and their range of tones is very pleasant. At present total market of herbal dyes is to the tune of US $ 1 billion and is growing tremendously at the rate of 12%per annum. Per capita consumption of dyes is 400g to 15 kg in developed and underdeveloped countries for their utility in paints, inks, textiles, polymers, etc. India is a major exporter of herbal dyes mostly due to ban on production of some of the synthetic dyes and intermediates in the developed countries due to pollution problem. Nature has gifted us more than 500 colour yielding plants. The present paper is an aid to a collective enquiry into the Indian dye yielding plants, their parts and chemical constituents