717 research outputs found
Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems
With the exponentially increasing volume of online data, searching and
finding required information have become an extensive and time-consuming task.
Recommender Systems as a subclass of information retrieval and decision support
systems by providing personalized suggestions helping users access what they
need more efficiently. Among the different techniques for building a
recommender system, Collaborative Filtering (CF) is the most popular and
widespread approach. However, cold start and data sparsity are the fundamental
challenges ahead of implementing an effective CF-based recommender. Recent
successful developments in enhancing and implementing deep learning
architectures motivated many studies to propose deep learning-based solutions
for solving the recommenders' weak points. In this research, unlike the past
similar works about using deep learning architectures in recommender systems
that covered different techniques generally, we specifically provide a
comprehensive review of deep learning-based collaborative filtering recommender
systems. This in-depth filtering gives a clear overview of the level of
popularity, gaps, and ignored areas on leveraging deep learning techniques to
build CF-based systems as the most influential recommenders.Comment: 24 pages, 14 figure
Toward enhancement of deep learning techniques using fuzzy logic: a survey
Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed
Text-based Sentiment Analysis and Music Emotion Recognition
Nowadays, with the expansion of social media, large amounts of user-generated
texts like tweets, blog posts or product reviews are shared online. Sentiment polarity
analysis of such texts has become highly attractive and is utilized in recommender
systems, market predictions, business intelligence and more. We also witness deep
learning techniques becoming top performers on those types of tasks. There are
however several problems that need to be solved for efficient use of deep neural
networks on text mining and text polarity analysis.
First of all, deep neural networks are data hungry. They need to be fed with
datasets that are big in size, cleaned and preprocessed as well as properly labeled.
Second, the modern natural language processing concept of word embeddings as a
dense and distributed text feature representation solves sparsity and dimensionality
problems of the traditional bag-of-words model. Still, there are various uncertainties
regarding the use of word vectors: should they be generated from the same dataset
that is used to train the model or it is better to source them from big and popular
collections that work as generic text feature representations? Third, it is not easy for
practitioners to find a simple and highly effective deep learning setup for various
document lengths and types. Recurrent neural networks are weak with longer texts
and optimal convolution-pooling combinations are not easily conceived. It is thus
convenient to have generic neural network architectures that are effective and can
adapt to various texts, encapsulating much of design complexity.
This thesis addresses the above problems to provide methodological and practical
insights for utilizing neural networks on sentiment analysis of texts and achieving
state of the art results. Regarding the first problem, the effectiveness of various
crowdsourcing alternatives is explored and two medium-sized and emotion-labeled
song datasets are created utilizing social tags. One of the research interests of Telecom
Italia was the exploration of relations between music emotional stimulation and
driving style. Consequently, a context-aware music recommender system that aims
to enhance driving comfort and safety was also designed. To address the second
problem, a series of experiments with large text collections of various contents and
domains were conducted. Word embeddings of different parameters were exercised
and results revealed that their quality is influenced (mostly but not only) by the
size of texts they were created from. When working with small text datasets, it is
thus important to source word features from popular and generic word embedding
collections. Regarding the third problem, a series of experiments involving convolutional
and max-pooling neural layers were conducted. Various patterns relating
text properties and network parameters with optimal classification accuracy were
observed. Combining convolutions of words, bigrams, and trigrams with regional
max-pooling layers in a couple of stacks produced the best results. The derived
architecture achieves competitive performance on sentiment polarity analysis of
movie, business and product reviews.
Given that labeled data are becoming the bottleneck of the current deep learning
systems, a future research direction could be the exploration of various data programming
possibilities for constructing even bigger labeled datasets. Investigation
of feature-level or decision-level ensemble techniques in the context of deep neural
networks could also be fruitful. Different feature types do usually represent complementary
characteristics of data. Combining word embedding and traditional text
features or utilizing recurrent networks on document splits and then aggregating the
predictions could further increase prediction accuracy of such models
Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey
Ubiquitous in-home health monitoring systems have become popular in recent
years due to the rise of digital health technologies and the growing demand for
remote health monitoring. These systems enable individuals to increase their
independence by allowing them to monitor their health from the home and by
allowing more control over their well-being. In this study, we perform a
comprehensive survey on this topic by reviewing a large number of literature in
the area. We investigate these systems from various aspects, namely sensing
technologies, communication technologies, intelligent and computing systems,
and application areas. Specifically, we provide an overview of in-home health
monitoring systems and identify their main components. We then present each
component and discuss its role within in-home health monitoring systems. In
addition, we provide an overview of the practical use of ubiquitous
technologies in the home for health monitoring. Finally, we identify the main
challenges and limitations based on the existing literature and provide eight
recommendations for potential future research directions toward the development
of in-home health monitoring systems. We conclude that despite extensive
research on various components needed for the development of effective in-home
health monitoring systems, the development of effective in-home health
monitoring systems still requires further investigation.Comment: 35 pages, 5 figure
Send frequency prediction on email marketing
O E-mail Marketing é uma forma de marketing direta que utiliza o e-mail como um meio de comunicação comercial pelo que numa perspetiva mais ampla, qualquer e-mail enviado a um potencial subscritor e atuais subscritores também pode ser considerado e-mail marketing.
Assim sendo, o subscritor vai receber várias comunicações ao longo do dia, reduzindo a visibilidade dos e-mails mais antigos com a entrada de novas comunicações e consequentemente, reduzindo as taxas de aberturas.
Tendo em conta que existem subscritores que preferem abrir e ler as suas comunicações de manhã, outros de tarde e alguns durante a noite, é necessário enviar uma comunicação que proporcione uma maior visibilidade que perpetue maiores taxas de abertura e uma maior captação de interesse do subscritor com a entidade que enviou uma comunicação.
Esta tese apresenta uma solução para enviar comunicações de marketing na altura certa aos subscritores ou potenciais subscritores. A sua contribuição consiste num modelo segmentado que utiliza um algoritmo tradicional de clustering baseado na informação trocada entre as empresas e os seus subscritores.
O modelo implementa posteriormente uma abordagem de ensemble paralelo utilizando técnicas como simple averaging e stacking com algoritmos de regressão treinados (RF, Linear Regression, KNN e SVR) e com um algoritmo de deep learning (RNNs) para determinar a melhor altura para enviar comunicações de e-mail. A implementação é executada utilizando um dataset fornecido pela empresa E-goi para treinar e testar a abordagem mencionada.
Os resultados obtidos nesta tese indicam que o algoritmo KNN é mais adequado para prever o melhor momento para enviar comunicações de e-mail dos algoritmos ML treinados. Das duas técnicas utilizadas para a abordagem do ensemble paralelo, o stacking é o mais adequado para prever o melhor momento para o envio das comunicações de e-mail.Email Marketing is a form of direct marketing that uses email as a means of commercial communication. In a broader perspective, any email sent to a potential subscriber and current subscribers can also be considered email marketing.
Therefore, the subscriber will receive several communications throughout the day, reducing the visibility of older emails with the entry of new communications and consequently reducing open rates.
Considering that there are subscribers who prefer to open and read their communications in the morning, others in the afternoon, and some at night, it is necessary to send a communication that provides the visibility that leads to higher open rates and capture the subscribers’ interest with the entity that sent the communication.
This thesis presents a solution to send marketing communications at the right time to subscribers or potential subscribers. Its contribution consists of a segmented model that uses a traditional clustering algorithm based on the information exchanged between companies and subscribers. The model then implements a parallel ensemble approach using simple averaging and stacking techniques with trained regression algorithms (RF, Linear Regression, KNN, and SVR) and a deep learning algorithm (RNNs) to determine the best time to send email communications. The implementation is executed using a dataset provided by the company E-goi to train and test the mentioned approach.
The results obtained in this thesis indicate that the KNN algorithm is better suited to predict the best time to send email communications of all the trained ML algorithms. Stacking is the most suitable for predicting the best time to send email communications of the two techniques used for the parallel ensemble approach
Applications of artificial intelligence in dentistry: A comprehensive review
This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Projects RTI2018-101674-B-I00 and PGC2018-101904-A-100, University of Granada project A.TEP. 280.UGR18, I+D+I Junta de Andalucia 2020 project P20-00200, and Fapergs/Capes do Brasil grant 19/25510000928-3. Funding for open-access charge: Universidad de Granada/CBUAObjective: To perform a comprehensive review of the use of artificial intelligence
(AI) and machine learning (ML) in dentistry, providing the community with a broad
insight on the different advances that these technologies and tools have produced,
paying special attention to the area of esthetic dentistry and color research.
Materials and methods: The comprehensive review was conducted in MEDLINE/
PubMed, Web of Science, and Scopus databases, for papers published in English language
in the last 20 years.
Results: Out of 3871 eligible papers, 120 were included for final appraisal. Study
methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other
ML techniques (n = 32), which were mainly applied to disease identification, image
segmentation, image correction, and biomimetic color analysis and modeling.
Conclusions: The insight provided by the present work has reported outstanding
results in the design of high-performance decision support systems for the aforementioned
areas. The future of digital dentistry goes through the design of integrated
approaches providing personalized treatments to patients. In addition, esthetic dentistry
can benefit from those advances by developing models allowing a complete
characterization of tooth color, enhancing the accuracy of dental restorations.
Clinical significance: The use of AI and ML has an increasing impact on the dental
profession and is complementing the development of digital technologies and tools,
with a wide application in treatment planning and esthetic dentistry procedures.Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674-B-I00
PGC2018-101904-A-100University of Granada project A.TEP. 280.UGR18Junta de Andalucia P20-00200Fapergs/Capes do Brasil grant 19/25510000928-3Universidad de Granada/CBU
Developing Student Model for Intelligent Tutoring System
The effectiveness of an e-learning environment mainly encompasses on how efficiently the tutor presents the
learning content to the candidate based on their learning capability. It is therefore inevitable for the teaching
community to understand the learning style of their students and to cater for the needs of their students. One
such system that can cater to the needs of the students is the Intelligent Tutoring System (ITS). To overcome
the challenges faced by the teachers and to cater to the needs of their students, e-learning experts in recent times
have focused in Intelligent Tutoring System (ITS). There is sufficient literature that suggested that meaningful,
constructive and adaptive feedback is the essential feature of ITSs, and it is such feedback that helps students
achieve strong learning gains. At the same time, in an ITS, it is the student model that plays a main role in
planning the training path, supplying feedback information to the pedagogical module of the system. Added to
it, the student model is the preliminary component, which stores the information to the specific individual
learner. In this study, Multiple-choice questions (MCQs) was administered to capture the student ability with
respect to three levels of difficulty, namely, low, medium and high in Physics domain to train the neural
network. Further, neural network and psychometric analysis were used for understanding the student
characteristic and determining the student’s classification with respect to their ability. Thus, this study focused
on developing a student model by using the Multiple-Choice Questions (MCQ) for integrating it with an ITS
by applying the neural network and psychometric analysis. The findings of this research showed that even
though the linear regression between real test scores and that of the Final exam scores were marginally weak
(37%), still the success of the student classification to the extent of 80 percent (79.8%) makes this student model
a good fit for clustering students in groups according to their common characteristics. This finding is in line
with that of the findings discussed in the literature review of this study. Further, the outcome of this research is
most likely to generate a new dimension for cluster based student modelling approaches for an online learning
environment that uses aptitude tests (MCQ’s) for learners using ITS. The use of psychometric analysis and
neural network for student classification makes this study unique towards the development of a new student
model for ITS in supporting online learning. Therefore, the student model developed in this study seems to be
a good model fit for all those who wish to infuse aptitude test based student modelling approach in an ITS
system for an online learning environment. (Abstract by Author
Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review
Context: User intent modeling is a crucial process in Natural Language
Processing that aims to identify the underlying purpose behind a user's
request, enabling personalized responses. With a vast array of approaches
introduced in the literature (over 13,000 papers in the last decade),
understanding the related concepts and commonly used models in AI-based systems
is essential. Method: We conducted a systematic literature review to gather
data on models typically employed in designing conversational recommender
systems. From the collected data, we developed a decision model to assist
researchers in selecting the most suitable models for their systems.
Additionally, we performed two case studies to evaluate the effectiveness of
our proposed decision model. Results: Our study analyzed 59 distinct models and
identified 74 commonly used features. We provided insights into potential model
combinations, trends in model selection, quality concerns, evaluation measures,
and frequently used datasets for training and evaluating these models.
Contribution: Our study contributes practical insights and a comprehensive
understanding of user intent modeling, empowering the development of more
effective and personalized conversational recommender systems. With the
Conversational Recommender System, researchers can perform a more systematic
and efficient assessment of fitting intent modeling frameworks
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