Music streaming services have transformed the way people listen to music in recent years. The current streaming services majorly rely on collaborative and hybrid filtering techniques, which ...predominantly recommend popular songs. However, most present systems lack musical contents, user taste changes, and novelty parameters. In this paper, we propose a music recommendation using reinforcement learning with personalizing the individual results. The proposed method implements a Q-learning model derived from the incremental reinforcement learning algorithm based on the cumulative reward from similar songs played and liked during the session. The user profile is modeled using implicit and explicit feedback from the individual musical interactions with the system. The cumulative reward obtained from the experimental outcomes demonstrates that a combination of reinforcement learning and personalized recommendation potentially broadens the scope of recommendations by including freshness and novelty. The experimental result shows average interaction time improvement of 35% compared with existing apps.
Natural Language Processing uses word embeddings to map words into vectors. Context vector is one of the techniques to map words into vectors. The context vector gives importance of terms in the ...document corpus. The derivation of context vector is done using various methods such as neural networks, latent semantic analysis, knowledge base methods etc. This paper proposes a novel system to devise an enhanced context vector machine called eCVM. eCVM is able to determine the context phrases and its importance. eCVM uses latent semantic analysis, existing context vector machine, dependency parsing, named entities, topics from latent dirichlet allocation and various forms of words like nouns, adjectives and verbs for building the context. eCVM uses context vector and Pagerank algorithm to find the importance of the term in document and is tested on BBC news dataset. Results of eCVM are compared with compared with the state of the art for context detrivation. The proposed system shows improved performance over existing systems for standard evaluation parameters.
Features play a crucial role in several computational tasks. Feature values are input to machine learning algorithms for the prediction. The prediction accuracy depends on various factors such as ...selection of dataset, features and machine learning classifiers. Various feature selection and reduction approaches are experimented with to obtain better accuracies and reduce the computational overheads. Feature engineering is designing new features suitable for a specific task with the help of domain knowledge. The challenges in feature engineering are presented for the computational music domain as a case study. The experiments are performed with different combinations of feature sets and machine learning classifiers to test the accuracy of the proposed model. Music emotion recognition is used as a case study for the experimentation. Experimental results for the task of music emotion recognition provide insights into the role of features and classifiers in prediction accuracy. Different machine learning classifiers provided varied results, and the choice of a classifier is also an important decision to be made in the proposed model. The engineered features designed with the help of domain experts improved the results. It emphasizes the need for feature engineering for different domains for prediction accuracy improvement. Approaches to design an optimized model with the appropriate feature set and classifier for machine learning tasks are presented.
Captcha are widely used to secure systems from automatic responses by distinguishing computer responses from human responses. Text, audio, video, picture picture-based Optical Character Recognition ...(OCR) are used for creating captcha. Text-based OCR captcha are the most often used captcha which faces issues namely, complex and distorted contents. There are attempts to build captcha detection and classification-based systems using machine learning and neural networks, which need to be tuned for accuracy. The existing systems face challenges in the recognition of distorted characters, handling variable-length captcha and finding sequential dependencies in captcha. In this work, we propose a segmentation-free OCR model for text captcha classification based on the connectionist temporal classification loss technique. The proposed model is trained and tested on a publicly available captcha dataset. The proposed model gives 99.80\% character level accuracy, while 95\% word level accuracy. The accuracy of the proposed model is compared with the state-of-the-art models and proves to be effective. The variable length complex captcha can be thus processed with the segmentation-free connectionist temporal classification loss technique with dependencies which will be massively used in securing the software systems.
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