229 research outputs found
Intelligent opinion mining and sentiment analysis using artificial neural networks
The article formulates a rigorously developed concept of opinion mining and sentiment analysis using hybrid neural networks. This conceptual method for processing natural-language text enables a variety of analyses of the subjective content of texts. It is a methodology based on hybrid neural networks for detecting subjective content and potential opinions, as well as a method which allows us to classify different opinion type and sentiment score classes. Moreover, a general processing scheme, using neural networks, for sentiment and opinion analysis has been presented. Furthermore, a methodology which allows us to determine sentiment regression has been devised. The paper proposes a method for classification of the text being examined based on the amount of positive, neutral or negative opinion it contains. The research presented here offers the possibility of motivating and inspiring further development of the methods that have been elaborated in this paper.Stuart, KDC.; Majewski, M. (2015). Intelligent opinion mining and sentiment analysis using artificial neural networks. Lecture Notes in Computer Science. 9492:103-110. doi:10.1007/978-3-319-26561-2_13S1031109492Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)Chen, H., Zimbra, D.: AI and opinion mining. IEEE Intell. Syst. 25(3), 74–80 (2010)Majewski, M., Zurada, J.M.: Sentence recognition using artificial neural networks. Knowl. Based Syst. 21(7), 629–635 (2008)Kacalak, W., Stuart, K.D., Majewski, M.: Intelligent natural language processing. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4221, pp. 584–587. Springer, Heidelberg (2006)Kacalak, W., Stuart, K., Majewski, M.: Selected problems of intelligent handwriting recognition. In: Melin, P., Castillo, O., RamĂrez, E.G., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. Advances in Soft Computing, vol. 41, pp. 298–305. Springer, Cancun (2007)Stuart, K.D., Majewski, M.: Selected problems of knowledge discovery using artificial neural networks. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007, Part III. LNCS, vol. 4493, pp. 1049–1057. Springer, Heidelberg (2007)Stuart, K., Majewski, M.: A new method for intelligent knowledge discovery. In: Castillo, O., Melin, P., Ross, O.M., Cruz, R.S., Pedrycz, W., Kacprzyk, J. (eds.) IFSA 2007. Advances in Soft Computing, vol. 42, pp. 721–729. Springer, Heidelberg (2007)Stuart, K.D., Majewski, M.: Artificial creativity in linguistics using evolvable fuzzy neural networks. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds.) ICES 2008. LNCS, vol. 5216, pp. 437–442. Springer, Heidelberg (2008)Stuart, K.D., Majewski, M.: Evolvable neuro-fuzzy system for artificial creativity in linguistics. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 46–53. Springer, Heidelberg (2008)Stuart, K.D., Majewski, M., Trelis, A.B.: Selected problems of intelligent corpus analysis through probabilistic neural networks. In: Zhang, L., Lu, B.-L., Kwok, J. (eds.) ISNN 2010, Part II. LNCS, vol. 6064, pp. 268–275. Springer, Heidelberg (2010)Stuart, K.D., Majewski, M., Trelis, A.B.: Intelligent semantic-based system for corpus analysis through hybrid probabilistic neural networks. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011, Part I. LNCS, vol. 6675, pp. 83–92. Springer, Heidelberg (2011)Specht, D.F.: Probabilistic neural networks. Neural Netw. 3(1), 109–118 (1990)Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991
Ultrasonic Flaw Detection Using Neural Network Models and Statistical Analysis: Simulation Studies
Flaw detection problems in ultrasonic NDE can be considered as two-class classification problems, i.e., determining whether a flaw is present or not present. To be practical, a flaw classification method must be able to handle the uncertainties associated with interference from grain noise which leads to poor signal-to-noise ratios (SNR). In this work, the use of neural network models and statistical correlation is demonstrated for one such detection/classification problem. In particular, based on simulation studies, we wish to establish practical strategies in detecting weak volumetric flaw signals corrupted by high grain noise. An example of this type that is of recent interest is the detection of “hard-alpha” inclusions in aircraft titanium components [1]. Both the feasibility and reliability of using these classifiers are assessed. This effort was carried out in parallel with another study [2] where more traditional signal processing approaches were taken
p3d – Python module for structural bioinformatics
<p>Abstract</p> <p>Background</p> <p>High-throughput bioinformatic analysis tools are needed to mine the large amount of structural data via knowledge based approaches. The development of such tools requires a robust interface to access the structural data in an easy way. For this the Python scripting language is the optimal choice since its philosophy is to write an understandable source code.</p> <p>Results</p> <p>p3d is an object oriented Python module that adds a simple yet powerful interface to the Python interpreter to process and analyse three dimensional protein structure files (PDB files). p3d's strength arises from the combination of a) very fast spatial access to the structural data due to the implementation of a binary space partitioning (BSP) tree, b) set theory and c) functions that allow to combine a and b and that use human readable language in the search queries rather than complex computer language. All these factors combined facilitate the rapid development of bioinformatic tools that can perform quick and complex analyses of protein structures.</p> <p>Conclusion</p> <p>p3d is the perfect tool to quickly develop tools for structural bioinformatics using the Python scripting language.</p
Quality of Service in IEEE 802.11ac and 802.11n Wireless Protocols with Applications in Medical Environments
Wireless computer networks are increasingly important as reliable
means of communication in medical environments. Evaluation of Quality of
Service (QoS) in wireless computer networks deployed in medical environments
can improve network performance and enhance utilization of resources. In this
study, the QoS offered by IEEE 802.11n and IEEE 802.11ac wireless protocols
was evaluated and compared using multiple point-to-point links for Voice Over
Internet Protocol (VoIP) traffic. QoS was evaluated based on Predictive Statistical
Diagnosis (PSD) and Probabilistic Neural Network (PNN). PSD and PNN based
QoS evaluation methods categorized the VoIP packets into low, medium and high
QoS types based on the packets' transmission delay, jitter, and percentage packet
loss ratio. Both PSD and PNN allowed QoS for VoIP to be quantified accurately.
It was shown that 802.11ac provides a higher QoS for VoIP transmission as
compared with IEEE 802.11n. The devised methods can be used in medical
environments for evaluation of wireless networks' QoS
Cavity Induced Interfacing of Atoms and Light
This chapter introduces cavity-based light-matter quantum interfaces, with a
single atom or ion in strong coupling to a high-finesse optical cavity. We
discuss the deterministic generation of indistinguishable single photons from
these systems; the atom-photon entanglement intractably linked to this process;
and the information encoding using spatio-temporal modes within these photons.
Furthermore, we show how to establish a time-reversal of the aforementioned
emission process to use a coupled atom-cavity system as a quantum memory. Along
the line, we also discuss the performance and characterisation of cavity
photons in elementary linear-optics arrangements with single beam splitters for
quantum-homodyne measurements.Comment: to appear as a book chapter in a compilation "Engineering the
Atom-Photon Interaction" published by Springer in 2015, edited by A.
Predojevic and M. W. Mitchel
Feature selection in the reconstruction of complex network representations of spectral data
Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitud
Generalized regression and feed-forward back propagation neural networks in modelling porosity from geophysical well logs
Assessment of lactation stage and breed effect on sheep milk fatty acid profile and lipid quality indices
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