Nomenclature and Benchmarking Models of Text Classification Models: Contemporary Affirmation of the Recent Literature

In this paper we present automated text classification in text mining that is gaining greater relevance in various fields every day Text mining primarily focuses on developing text classification systems able to automatically classify huge volume of documents comprising of unstructured and semi structured data The process of retrieval classification and summarization simplifies extract of information by the user The finding of the ideal text classifier feature generator and distinct dominant technique of feature selection leading all other previous research has received attention from researchers of diverse areas as information retrieval machine learning and the theory of algorithms To automatically classify and discover patterns from the different types of the documents 1 techniques like Machine Learning Natural Language Processing NLP and Data Mining are applied together In this paper we review some effective feature selection researches and show the results in a table for

Error Function Attack of chaos synchronization based encryption schemes

Different chaos synchronization based encryption schemes are reviewed and compared from the practical point of view. As an efficient cryptanalysis tool for chaos encryption, a proposal based on the Error Function Attack is presented systematically and used to evaluate system security. We define a quantitative measure (Quality Factor) of the effective applicability of a chaos encryption scheme, which takes into account the security, the encryption speed, and the robustness against channel noise. A comparison is made of several encryption schemes and it is found that a scheme based on one-way coupled chaotic map lattices performs outstandingly well, as judged from Quality Factor

Machine learning approach for personalized recommendations on online platforms: uniplaces case study

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe goal of this project is to develop a model to personalize the user recommendations of an online marketplace named Uniplaces. This online business offers properties for medium and long-term stays, where landlords can directly rent their place to customers (mainly students). Whenever a student makes a reservation, the booking must be approved by the property owner. The current acceptance rate is 25%. The model is a response to this low acceptance rate, and it will have to show to each student the properties that are more likely to be accepted by the landlord. As a secondary objective, the model seeks to identify the reasons behind the landlord’s decision to accept or reject bookings. The model will be constructed using information from the users, landlord and the property itself kindly provided by Uniplaces. This information will pre-process with data cleaning, transformation and features reduction (where two techniques were applied: dimensionality reduction, features selection). After the data processing, several models were applied to the normalized data. The predictive models that will be applied are already being used on other online markets and platforms like Airbnb, Netflix or LinkedIn, namely Support Vector Machine, Neural Networks, Decision Tree, Logistic Regression and Gradient Boosting. The probability of acceptance proved to be very easy to predict, all the models predict 100% of the test dataset when using the Principal Component Analysis as the Dimensionality Reduction technique. This can be explained mainly by the fact that the new calculated features have a strong correlation with the target variable. All the algorithms predict 100% of the target variable when using Principal Component Analysis as a technique of dimensionality reduction

Where there is life there is mind: In support of a strong life-mind continuity thesis

This paper considers questions about continuity and discontinuity between life and mind. It begins by examining such questions from the perspective of the free energy principle (FEP). The FEP is becoming increasingly influential in neuroscience and cognitive science. It says that organisms act to maintain themselves in their expected biological and cognitive states, and that they can do so only by minimizing their free energy given that the long-term average of free energy is entropy. The paper then argues that there is no singular interpretation of the FEP for thinking about the relation between life and mind. Some FEP formulations express what we call an independence view of life and mind. One independence view is a cognitivist view of the FEP. It turns on information processing with semantic content, thus restricting the range of systems capable of exhibiting mentality. Other independence views exemplify what we call an overly generous non-cognitivist view of the FEP, and these appear to go in the opposite direction. That is, they imply that mentality is nearly everywhere. The paper proceeds to argue that non-cognitivist FEP, and its implications for thinking about the relation between life and mind, can be usefully constrained by key ideas in recent enactive approaches to cognitive science. We conclude that the most compelling account of the relationship between life and mind treats them as strongly continuous, and that this continuity is based on particular concepts of life (autopoiesis and adaptivity) and mind (basic and non-semantic)