There are a vast number of quantitative and qualitative technological forecasting methods. In the last decade, advanced quantitative technological forecasting methods based on the various ...applications of data science approaches have been proposed. Text mining is one of the key approaches used to examine large datasets consisting of scientific publications and patent documents with the aim of offering foresight for a selected area. However, the existing related studies either perform a qualitative approach by analysing the recent data to identify the emerging topics or use extrapolation techniques to predict the future values of some statistical terms or the future frequency of some important keywords. In this study, different from such related studies, we propose a deep learning-based framework to predict future co-similarity matrix representing the possible new and disappearing interactions between the words in the future. For this purpose, word vectors are generated using a word embedding technique and the temporal changes of the associations between the words are modelled using Long Short-Term Memory networks for the future estimation of the word embedding matrix. The text mining area is chosen as a case study. The clusters of the terms extracted from the predicted word embedding matrices were analysed and potentially emerging areas were identified for different prediction horizon lengths. The accuracy of the proposed model was analysed based on a set of evaluation metrics that measure the amount of overlapping between the actual and predicted word maps. The quantitative analysis showed that the proposed system can successfully identify the emerging and disappearing areas and can be used as a decision-making tool for the future projection of other areas.
•The paper proposes a framework to predict co-similarity matrix for technological forecasting.•The text mining area is chosen as a case study to illustrate emerging areas for different prediction horizon lengths.•The deep learning-based model can successfully identify the emerging and disappearing areas.•The study utilizes techniques such as word embedding and Long Short-Term Memory networks.•The framework can be used as a decision-making tool for the future projection of technological areas.
•A general framework that analyses social media-based product feedback is proposed.•Deep neural networks and natural language processing methods are utilized.•Proposed system is illustrated for ...product analysis on the Google Glass case.•The system offers a detailed product analysis framework for decision makers.
Social media platforms are considered one of the most effective intermediaries for companies to interact with consumers. Social media-based decision support systems for the marketing domain are highly developed, but product development and innovation-oriented studies remain limited. This study offers a novel approach which utilises opinion retrieval theme along with sentiment analysis to support the decision-making process for product analysis and development. To achieve this aim, we propose an end-to-end social media-based opinion retrieval system and utilise machine learning and natural language processing techniques. Google Glass is chosen as a use-case as this product was unable to achieve its commercial targets despite its superior technological offerings. We design a multi-task deep neural network architecture for the training of sentiment prediction and opinion detection tasks. We first divide the tweets containing certain useful opinions and suggestions into two categories based on their sentiment labels. The negative tweets are analysed to identify product-related concerns, whereas the positive and neutral tweets are used to extract innovative ideas and identify new use cases for product development. We visualise and interpret the clusters of keywords extracted from each sentiment label group. Apart from methodological contributions, this study offers practical contributions for the next generations of smart glasses.
Accessing to the best matching multimedia data is a trending topic due to the enormous amount of demand from people for movies, online TV series, videos etc. Advertising/Introducing form/image of ...such multimedia applications is important to give the key information to the audience. Sometimes a movie poster may play an important role to present the movie genre correctly. In recent years, Convolutional Neural Networks (CNN) as a deep learning architecture achieved state of-the- art performance in many image processing and recognition applications. In this paper, we implement transfer learning and fine-tuning methods on top of Google Inception-v3 algorithm, which is one of the most popular CNN architectures in this domain, and present comparative results of these methods in classifying the movie genre on a dataset consisting of Turkish movie posters. The obtained results show that fine tuning method performs better than pure CNN and transfer learning models on movie genre classification task.
The IEEE 802.1 Time-Sensitive Networking task group standardized the Link-local Registration Protocol. This standard outlines the protocols, procedures needed to replicate changes made to a ...registration database from one end of a point-to-point link to the other. It is part of resource management component in Time-Sensitive Networking tool set. This paper aims to design and implement the Link-local Registration Protocol along with an application instance in Linux environment. Majority of the specifications and features listed in the standard are implemented. Experimental results include communication details between two systems running Link-local Registration Protocol.
Software system admins depend on log data for understanding system behavior, monitoring anomalies, tracking software bugs, and malfunctioning detection. Log analysis based on machine learning ...techniques enables to transform of raw logs into meaningful information that helps the DevOps team and administrators to solve problems. Al ensures to group similar logs together and keeps periodic logs more organized and sorted, allowing us to get to where we need to look faster. In this paper, we present a log classification system on log data generated by VoIP (Voice over Internet Protocol) soft-switch product. In this way, we targeted to detect the problem, direct it to the relevant department, allocate resources, and solve software bugs faster and more efficiently. Machine learning algorithms such as Linear Classifiers, Support Vector Machines, Decision Tree, Random Forest, Boosting, K-Nearest Neighbors, and Multilayer Perceptron are used for log classification.
One of the important costly items in software projects is effort and time to fix the found problems. Several reporting software tools are used to record and track software bugs. In general, the file ...or files where the code change is made for the solution of the problem is/are associated with the created bug record and tracked through the software version control systems. At this point, it would be time-saving to utilize Artificial Intelligence methods to make use of the solution of past problems and predict which code file will need to be touched for the fixing of newly found problem in future. In this study, the design and implementation of an artificial intelligence supported virtual assistant which predicts defective software files will be explained.
The resources used for detecting a software problem in embedded systems, which are preferred in real time applications, are limited. Therefore, having the ability to track code in real time in ...embedded systems is critical especially for software design and technical support teams. In this study, the design of a code tracking tool operating at the machine language level for embedded systems and its implementation on the VoIP switch will be explained.
Mobile Number Portability service has been launched in many countries and supported by all mobile and some fixed telecom operators in the world. In some fixed operators, this service regulation may ...have an interaction with national VoIP switches for call forwarding scenarios based on ISUP telecom protocol standard. In this work, we share our experience and solution approach for such a case mentioned above which occurred in customer live site.
COVID-19 can directly or indirectly cause lung involvements by crossing the upper airways. It is essential to quickly detect the lung involvement condition and to follow up and treat these patients ...by early hospitalization. In recent COVID-19 diagnosis procedure, PCR testing is applied to the samples taken from the patients and a quarantine period is applied to the patient until the test results are received. As a complement to PCR tests and for faster diagnosis, thin-section lung computed tomography (CT) imaging is used in COVID-19 patients. In this study, it is aimed to develop a method that is as reliable as CT, and compared to CT, less risky, more accessible, and less costly for the diagnosis of COVID-19 disease. For this purpose, first speech and cough sounds from the oral, laryngeal and thoracic regions of COVID-19 patients and healthy individuals were obtained with the multi-channel voice recording system we proposed, the obtained data were processed with machine learning methods and their accuracies in COVID-19 diagnosis were presented comparatively. In our study, the best results were obtained with the features extracted from the cough sounds taken from the oral region.
A wireless sensor network system using 6LoWPAN Demir, Bilal; Ayrancioglu, Gokhan; Gezer, Cengiz ...
2016 National Conference on Electrical, Electronics and Biomedical Engineering (ELECO),
2016-Dec.
Conference Proceeding
Internet of Things is getting wide-spread attention since it became easily accessible with the low costs nodes. In a typical internet of things application, the data sensed by nodes is usually sent ...to a central device which collects the information and can act as a gateway towards other networks. In this way the data can be processed in order to command the actuators to perform special tasks. In such networks 6LowPAN together with RPL protocol can be used as an enabler technology. In this study we implemented a 6LowPAN network using Contiki operating system in order to collect information coming from different sensors residing in nodes and stored this information in a database to be accessed by Internet. In our 6LowPAN network nodes send UDP packets to a UDP Server located in a low cost rapid prototyping device.
