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Background/introduction The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis. Previous article in issueNext article in issue Keywords Urdu sentiment analysisPre-processingSentiment lexiconDatasetsCorpusUrdu sentiment classificationSemantic orientation

A typical grid connected wind energy system is always associated with the nonlinear dynamics, uncertainties and external disturbances. Moreover as per the adopted grid codes and in the occurrence of specific faults the wind energy system should remain connected to the grid for a defined time period. Keeping in view all the effects this article proposes a high performance fault tolerant fractional order control system for the rotor side converter of a doubly fed induction generator (DFIG). The base line controller is formulated using novel fractional order sliding manifolds and the stability of the closed loop is ensured using fractional order Lyapunov theorem. As a case study the faults are introduced in the rotor current sensors and a model based fault tolerant rotor current estimation algorithm is proposed which is based on the stator measured currents and the voltages. Numerical results are presented to show the superiority of the proposed base line fractional order control method over the classical sliding mode control system in normal operating conditions. Finally the proposed algorithm is tested under the rotor current sensors fault and the results under faulty conditions are compared to that without faults.

Waterborne biodegradable polyurethane–urea (WPUU) elastomer is a revolutionary step in the field of green polymer chemistry. In the present study, emulsifier–free biodegradable WPUU elastomer was synthesized by using lysine as an internal emulsifier as well as a chain extender. WPUU elastomer was used as the stimuli–responsive drug release matrix, loaded by cisplatin as a model anticancer drug. Sustained drug release was studied by changing pH (4.4–7.4), drug release medium, and NCO/OH ratio (3–4). Furthermore, significantly inhibitory effect against Sw–620 cancer lines was observed. Highest cumulative drug release of 71.9% was observed at pH of 7.4 in phosphate buffer saline (PBS) by the system having NCO/OH = 3 for 250 h. The oxidative degradation was evaluated in CoCl2/H2O2 solution. Mechanical properties were tested by using the mechanical testing machine. The synthesized WPUU films exhibited high thermal stability, hardness, tensile strength and hydrophobicity.

Artificial Intelligence in the form of machine learning is employed in games to control non-human computer-players, agents or bots. However, most of these games such as Atari took place in 2D environments that were not fully observable to the agents. Currently, it is of extreme significance to employ such machine learning techniques and methods in 3D environments such as Doom. Therefore, In this paper, we train agents on the health gathering scenario of the classical first-person shooter game Doom by first presenting the Direct Future Prediction to train an agent that uses a simple architecture with no additional supervisory signals, then differentiate and compare the performance of the agents trained by using several different machine learning techniques, and the AI reinforcement learning platform ‘VizDoom’, a 3D partially observable environment, with interesting enhanced properties that makes agents to stand out from inbuilt AI agents and human players. We have continued to use computer games as a benchmark for the performance of AI as having been so successful in the past. We also compared the results of our findings to conclude the performance of the agents trained with different machine learning techniques. The agents performed well against both human players and inbuilt game agents.