A NOVEL MACHINE LEARNING SYSTEM FOR SENTIMENT ANALYSIS AND EXTRACTION
Osama Mohammad Rababah1, and Nour Alokaily2 ,1,2The University of Jordan, Amman, Jordan
The huge volume of online reviews makes it difficult for a human to process and extract all significant information to make decisions. As a result, there has been a trend to develop systems that can automatically summarize opinions from a set of reviews. In this respect, the automatic classification and information extraction from users’ comments, also known as sentiment analysis (SA) becomes vital to offer users the best responses to users’ queries, based on their preferences. In this paper, a novel system hat offers personalized user experiences and solves the semantic-pragmatic gap was presented. Having a system for forecasting sentiments might allow us, to extract opinions from the internet and predict online user’s favorites, which could determine valuable for commercial or marketing research. The data used belongs to the tagged corpus positive and negative processed movie reviews introduced by Pang and Lee[1]. The results show that even when a small sample is used, sentiment analysis can be done with high accuracy if appropriate natural language processing algorithms applied.
Machine Learning, Big Data, Natural Language Processing, Sentiment Analysis
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https://aircconline.com/csit/papers/vol9/csit91330.pdf
PREDICTION AND CAUSALITY ANALYSIS OF CHURN USING DEEP LEARNING
Muzaffar Shah, Darshan Adiga, Shabir Bhat and Viveka Vyeth Datoin Bangalore, India
In almost every type of business a retention stage is very important in the customer life cycle because according to market theory, it is always expensive to attract new customers than retaining existing ones. Thus, a churn prediction system that can predict accurately ahead of time, whether a customer will churn in the foreseeable future and also help the enterprises with the possible reasons which may cause a customer to churn is an extremely powerful tool for any marketing team. In this paper, we propose an approach to predict customer churn for nonsubscription based business settings. We suggest a set of generic features that can be extracted from sales and payment data of almost all non-subscription based businesses and can be used in predicting customer churn. We have used the neural network-based Multilayer perceptron for prediction purposes. The proposed method achieves an F1-Score of 80% and a recall of 85%, comparable to the accuracy of churn prediction for subscription-based business settings. We also propose a system for causality analysis of churn, which will predict a set of causes which may have led to the customer churn and helps to derive customer retention strategies.
churn Analysis, Causality Analysis, Machine Learning, Business Analytics , Deep Neural Network
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https://aircconline.com/csit/papers/vol9/csit91312.pdf
COGNITIVE CITIES AN ARCHITECTURAL FRAME WORK FOR THE CITIES OF THE FUTURE
Cristiana Carvalho1, Filipe Cabral Pinto1, Isabel Borges1, Gonçalo Machado1and Ilídio Oliveira2 1Altice Labs, Aveiro, Portugal , 2Telecomunicações e Informática, University of Aveiro, Portugal
Digital transformation has changed management models in cities. The use of tools supported by information and communication technologies has facilitated the planning and control of the urban space allowing a rapprochement between the city and the citizens. This proximity is exponentiated with the advent of the Internet of things becoming possible to permanently know the state of the city and to act on the different infrastructures in a dynamic way. This paper proposes the use of Machine Learning techniques to enhance city management by predicting behaviours and automatically adapt rules mechanisms in order to mitigate city problems contributing to the improvement of lives living or visiting municipalities.
Architecture, Learning City, Smart Cities, Machine Learning, Big Data
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https://aircconline.com/csit/papers/vol9/csit91314.pdf
PREDICTION OF WORKPIECE QUALITY: AN APPLICATION OF MACHINE LEARNING IN MANUFACTURING INDUSTRY
Günther Schuh1, Paul Scholz2, Sebastian Schorr3, Durmus Harman2, Matthias Möller4, Jörg Heib4, Dirk Bähre3 1,2RWTH Aachen University Aachen, Germany , 3Saarland University, Saarbrücken, Germany 4 Bosch Rexroth AG, Bexbacher Street, Germany
A significant amount of data is generatedand could be utilized in order to improve quality, time, and cost related performance characteristics of the production process. Machine Learning (ML) is considered as a particularly effective method of data processing with the aim of generating usable knowledge from data and therefore becomes increasingly relevant in manufacturing. In this research paper, a technology framework is created that supports solution providers in the development and deployment process of ML applications. This framework is subsequently successfully employed in the development of an ML application for quality prediction in a machining process of Bosch Rexroth AG.For this purpose the 50 mostrelevant features were extracted out of time series data and used to determine the best ML operation. Extra Tree Regressor (XT) is found to achieve precise predictions with a coefficient of determination (R 2) of constantly over 91% for the considered quality characteristics of a boreof hydraulic valves.
Technology Management Framework, Quality Prediction, Machine Learning, Manufacturing, Workpiece Quality
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https://aircconline.com/csit/papers/vol9/csit91316.pdf
AN ARTIFICIAL NEURAL NETWORK APPROACH FOR THE CLASSIFICATION OF HUMAN LOWER BACK PAIN
Shubham Sharma and Rene V.Mayorga ,University of Regina, Canada
In today’s world, the problem of lower back pain is one of the fastest growing crucial ailments to deal with. More than half of total population on the earth, suffers from it at least once in a lifetime. Human Lower Back Pain symptoms are commonly categorized as Normal or Abnormal. In order to remedy Human Lower Back Pain, with the growth of technology over the time, many medical methods have been developed to diagnose and cure this pain at its earliest stage possible. This study aims to develop two Machine Learning (M.L.) models which can classify Human Lower Back Pain symptoms in a human body using non-conventional techniques such as Feed forward/Back propagation Artificial Neural Networks, and Fully Connected Deep Networks. An Automatic Feature Engineering technique is implemented to extract featured data used for the classification. The proposed models are compared with respect to a Support Vector Machine model; considering different performance parameters.
Machine Learning, Artificial Neural Networks, Fully Connected Deep Networks, Support Vector Machine, Lower Back Pain, Automatic Feature Engineering technique.
For More Details :
https://aircconline.com/csit/papers/vol9/csit91313.pdf
CONSTRUCTION OF AN ORAL CANCER AUTO CLASSIFY SYSTEM BASED ON MACHINE LEARNING FOR ARTIFICIAL INTELLIGENCE
Meng-Jia Lian1, Chih-Ling Huang2, Tzer-Min Lee1,3 , 1,2Kaohsiung Medical University, Kaohsiung, Taiwan 3 National Cheng Kung University Medical College, Taiwan
Oral cancer is one of the most widespread tumors of the head and neck region. An earlier diagnosis can help dentist getting a better therapy plan, giving patients a better treatment and the reliable techniques for detecting oral cancer cells are urgently required. This study proposes an optic and automation method using reflection images obtained with scanned laser pico-projection system, and Gray-Level Co-occurrence Matrix for sampling. Moreover, the artificial intelligence technology, Support Vector Machine, was used to classify samples. Normal Oral Keratinocyte and dysplastic oral keratinocyte were simulating the evolvement of cancer to be classified. The accuracy in distinguishing two cells has reached 85.22%. Compared to existing diagnosis methods, the proposed method possesses many advantages, including a lower cost, a larger sample size, an instant, a non-invasive, and a more reliable diagnostic performance. As a result, it provides a highly promising solution for the early diagnosis of oral squamous carcinoma.
Oral Cancer Cell, Normal Oral Keratinocyte (NOK), Dysplastic oral keratinocyte (DOK),GrayLevel Co-occurrence Matrix (GLCM), Scanned Laser Pico-Projection (SLPP), Support Vector Machine (SVM), Machine-Learning
For More Details :
https://aircconline.com/csit/papers/vol9/csit90903.pdf