A BUSINESS MODEL FRAMEWORK FOR INTERNET OF THINGS

The Internet of Things (IoT) generates new business opportunities by connecting physical objects with a multitude of sensors. IoT research mainly focused on technology and business models are relatively unexplored, although developing IoT business models is important for successful IoT service. This study aims to investigate what are the elements to be taken in order to create a business model for IoT. To address this issue, we review the literature on the creation of business model for IoT and propose a generic business model framework for IoT business through the literature analysis. The IoT allows existing business models to change and new business to emerge. This research acts as a starting point for designing or developing business models for IoT services

Generate Contextual Insight of Product Review Using Deep LSTM and Word Embedding

AbstractNowadays, in every day live, majority people face in many internet options. For example, what meal to eat, what news to read, what vehicle to ride, what the best path to travelling, what the best group in social network to joint, what the best video to watch, what the best video in YouTube to watch and etc. The best way to recommend the internet content to customer is by using recommender system. Recommender system calculate product recommendation by detecting user behaviour in the past. The user behaviour in the past was being variable to compute similarity between many customers. One of the majority user behaviour is in the term of document. Most of document interpret model in recommender system use traditional NLP model such as TF-IDF, LDA model. According to NLP point of view, traditional NLP face the weakness in contextual understanding. Aims to handle the problem on above, we proposed novel model to generate contextual understanding by involve two important aspect considered subtle word and word sequence. We implemented word embedding based on GLOVE and detecting word sequential using RNN-LSTM. According to qualitative evaluation report, our model successful to capture contextual insight of the document of movie review by IMDB. This model suitable to integrated with latent factor based on matrix factorization to generate product recommendation in Collaborative filtering model.</jats:p

Deep Contextual of Document Using Deep LSTM Meet Matrix Factorization to Handle Sparse Data: Proposed Model

Abstract Recommender system is important tool in big data era. It has responsible to make suggestion about product or service automatically for web application or mobile. In everyday utility, we cannot escape for information about food, travelling, social network, ticketing, news and etc. What the best choice for customer necessary is recommender system task to provide relevant information. Collaborative filtering is most useful recommender system technique in which considering user behaviour in the past to calculate recommendation. The first generation of collaborative filtering exploit statistical approach to calculate product recommendation. However, traditional collaborative filtering facing serious problem in scalability, accuracy and shortcoming in large data. Model based in the second generation of collaborative filtering to produce product recommendation where this model rely on matrix factorization to produce recommendation. Model based proven better performance over memory based. However, model-based performance degrades significantly when met with sparse data due the number of rating are very small. This problem popular called sparse data problem. Several methods proposed by researchers to handle sparse data problem. Mostly of them exploit text document to increase recommendation performance. However, majority of model fail to gain text document understanding. This study proceeds ongoing process with several stage. First, develop model to interpreted text document using LSTM aims to capture contextual understanding of document. Second, integrated LSTM with matrix factorization. This step aims to produce rating prediction considering text document of the product. The first step completely finished. According to experiment report, this model success to capture contextual of the document then transform into 2D space text document representation. For the further research, we are going to integrated with matrix factorization and evaluation result of rating prediction using RMSE metric evaluation.</jats:p

Integrating Supervised Classification in Social Participation Systems for Disaster Response. A Pilot Study

The recent evolution of Information and Communication Technology (ICT) and mobile devices has strongly encouraged social participation as a tool for decision-support systems. These social participation tools are labelled as Participatory Geographic Information System (PGIS). The use of these tools has also extended to several domains – such as natural disasters, humanitarian crises, political conflicts – with the main aim to help affected populations and provide useful information for survival. Nonetheless, social participation tools present some drawbacks for managing non-structured information retrieved from large databases and Social Networks. The limitations concern either the need to understand knowledge in (almost) real time or data classification according to a specific domain. The present work aims at understanding the use of supervised classification models in situations of emergencies (i.e. disaster response) to classify message requests asking for/offering to help. To achieve the above aim we use machine learning techniques to compare classification models and evaluate their effectiveness and potentials to integrate them into existing PGIS systems. Main results suggest the existence of a relatively high accuracy of test and training classification by employing Random Forest, Neural Networks and Support Vector Machine (SVM) models. We argue in favour of supervised classification for its usefulness as a tool to be integrated in social participation for disaster response