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7th Int Conf on Soft Comp Models in Industrial and Environm Applications

作者： Rivera, Antonio J. Garcia-Domingo, B. del Jesus, M. J. Aguilera, J. Dept. of Computer Science University of Jaén Jaen Spain Dept. of Electronics and Automation Engineering University of Jaén Jaen Spain

ISBN: (纸本)9783642329210

Concentrating Photovoltaic (CPV) technology attempts to optimize the efficiency of solar energy production systems and models for determining the exact module performance are needed. In this paper, a CPV module is studied by means of atmospheric conditions obtained using an automatic test and measuring system. (CORBFN)-R-2, a cooperative-competitive algorithm for the design of radial basis neural networks, is adapted and applied to these data obtaining a model with a good level of accuracy on test data, improving the results obtained by other methods considered in the experimental comparison. These initial results are promising and the obtained model could be used to work out the maximum power at the CPV reporting conditions and to analyze the performance of the module under any conditions and at any moment.

关键词： Concentrating Photovoltaic Technology Neural Networks Evolutionary Computation Soft-Computing Regression

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Using temporal video annotation as a navigational aid for video browsing 10

Using temporal video annotation as a navigational aid for vi...

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Adjunct proceedings of the 23nd annual ACM symposium on User interface software and technology

作者： Müller, Stefanie Miller, Gregor Fels, Sidney Dept. of Automation and Computer Science University of Applied Sciences Harz Canada Dept. of Electrical and Computer Engineering University of British Columbia Canada

ISBN: (纸本)9781605588438

Video is a complex information space that requires advanced navigational aids for effective browsing. The increasing number of temporal video annotations offers new opportunities to provide video navigation according to a user's needs. We present a novel video browsing interface called TAV (Temporal Annotation Viewing) that provides the user with a visual overview of temporal video annotations. TAV enables the user to quickly determine the general content of a video, the location of scenes of interest and the type of annotations that are displayed while watching the video. An ongoing user study will evaluate our novel approach.

关键词： video annotation video search video navigation video browsing

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Point-of-Interest Recommendation using Supervised Link Prediction

Point-of-Interest Recommendation using Supervised Link Predi...

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2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

作者： Cakmak, Elif Gul Kaya, Buket Kaya, Mehmet Firat University Dept. of Computer Engineering Elaziǧ Turkey Firat University Dept. of Electronics and Automation Elaziǧ Turkey

ISBN: (纸本)9781665416566

Location-based social networks are a platform that users use to share their information. One of the reasons for using this platform is that POIs are checked in by users and in a sense that POI is recommended to next visitors. The aim of this paper is to find out whether people from the same country visit the same POIs and recommend them accordingly. For this purpose, firstly, a bi-partite network consisting of users and POIs is created. Then, new POIs are recommended to the user regarding the link prediction method over this network. The experimental results on real data obtained from the Foursquare social network showed that the proposed method achieved high accuracy values. © 2021 IEEE.

关键词： Forecasting

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A Paper Recommendation System Based on User Interest and Citations 1

A Paper Recommendation System Based on User Interest and Cit...

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1st International Informatics and Software engineering Conference, IISEC 2019

作者： Bulut, Betul Kaya, Buket Kaya, Mehmet Firat University Dept. of Computer Engineering Elaziǧ Turkey Firat University Dept. of Electronics and Automation Elaziǧ Turkey

ISBN: (纸本)9781728139920

It is difficult for researchers to access useful scientific articles from an excessive number of articles. This difficulty has led to article recommendation systems. Many studies have been conducted on the article recommendation. Article recommendation studies in the literature generally do not take into account the interest of users. The innovation of this study is to propose research articles by using bibliography and creating a user profile. The proposed approach aims to personalize scientific recommendations based on article-bibliographic relationships and user profile. In our study, candidate recommendation articles were clustered by K-Means clustering method according to their bibliographies. In our study, K-NN algorithm is used to determine which cluster is closer to the user's profile. Once the cluster to which the user belongs is determined, other articles in the cluster are recommended to the user. Experimental results show that the method is successful. © 2019 IEEE.

关键词： Bibliographies

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A Deep Learning Model for More Descriptive Image Captioning

A Deep Learning Model for More Descriptive Image Captioning

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Doner, Tunahan Kaya, Buket Firat University Dept. of Computer Engineering Elazig Turkey Firat University Dept. of Electronics and Automation Elazig Turkey

ISBN: (纸本)9798350369106

Describing images with natural and fluent sentences is recognized as a challenging problem in computer vision and natural language processing. The limited number of studies in this area, especially in the Turkish language, and the fact that the existing datasets usually contain generalized descriptions made this research necessary. In this study, a rich dataset in Turkish language was created and this dataset was developed by optimizing both the natural structure of the language and the sentence lengths. An accuracy rate of 36% was achieved in the training process and 31% in the testing process. As a result, the generated dataset aims to contribute to Turkish image recognition models to produce more natural and detailed sentences. © 2024 IEEE.

关键词： Modeling languages

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Contrastive Self-Supervised Learning for Cocoa Disease Classification 10

Contrastive Self-Supervised Learning for Cocoa Disease Class...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Khalid, Usman Kaya, Buket Kaya, Mehmet Firat University Dept. of Computer Engineering Elaziǧ Turkey Firat University Dept. of Electronics and Automation Elaziǧ Turkey

ISBN: (纸本)9798350363104

Self-supervised learning has shown significant improvement within several disciplines including the health sectors and areas where acquisition of annotated data is difficult. This study focuses on the application of self-supervised learning in tackling cocoa disease detection and classification challenges by taking cocoa swollen shoot virus disease (CSSVD) and anthracnose. Self-supervised learning has proven to offer resilience against both OOD and adversarial attacks and as an alternative to the cost involved in the annotation process of the vast amount of data. This work leverages a contrastive self-supervised learning pretext model trained on cocoa leaves and pods, a remarkable performance was shown when we fine-tuned and pre-trained on a downstream task. Several works are being done on the subject of self-supervised learning and fully-supervised learning. In order to get the best out of these two techniques, we propose a hybrid approach which will combine both SLL and fully supervised learning. Thus in any deep learning project regardless of the amount of annotated dataset around, a pretext model should be developed from the dataset which can then be fined-tuned using the entire dataset at hand. Three models were developed and the best come in as a result of fine-tuning the learnt features using the entire dataset. After performing the downstream task (classification of cocoa diseases) a validation accuracy of 87%, 72% and 69% for the fine-tune, fully-supervised and pre-trained respectively were obtained. This approach holds promise for enhancing the productivity in crop management, particularly in regions like Ghana and Ivory Coast where their GDP relies on cocoa production © 2024 IEEE.

关键词： Self-supervised learning

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Point-of-Interest Recommendation in Location-Based Social Networks 1

Point-of-Interest Recommendation in Location-Based Social Ne...

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1st International Informatics and Software engineering Conference, IISEC 2019

作者： Cakmak, Elifgul Kaya, Buket Kaya, Mehmet Firat University Dept. of Computer Engineering Elaziǧ Turkey Firat University Dept. of Electronics and Automation Elaziǧ Turkey

ISBN: (纸本)9781728139920

This article investigates the problem of proposing new locations to users who have previously been informed about location-based social networks. In the study, user profiles (past location information), the popularity of the place and the time of preference of the place were obtained. Using this information, a different place is offered to the users. However, when users have a different location (a different city or country), it is provided to recommend one of the point of interests designated by categories according to the pre-made check-in details. Social media has been a component of daily life of users. Applications such as Foursquare/Swarm constitute the data source of the communication that geographically contains location data. This study analyzes the personal behaviors of the users using social networks for the spaces and reveals the characteristics of the spaces and people by past notification activities through the applications made in the past. The experiments on real foursquare data showed the usability of the proposed method and achieved results with high accuracy. © 2019 IEEE.

关键词： Location

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E-Mail Spam Detection Using BERT and LSTM

E-Mail Spam Detection Using BERT and LSTM

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Erkus, Celal Kaya, Buket Firat University Dept. of Computer Engineering Elaziǧ Turkey Firat University Dept. of Electronics and Automation Elazig Turkey

ISBN: (纸本)9798350369106

The quick advancement of today's digital world and our daily reliance on the internet have a big impact on our lives. Especially in these days when automation is very easy, fraud and fraudulent activities against users have become quite common. One of the most common examples of these activities is fraud attempts through spam e-mails. Spam e-mails aim to hijack people's information, spread malware and defraud people by playing with their perceptions. Various spam detection methods and algorithms have been developed to prevent these problems. In this study, two models are proposed to detect spam e-mails using natural language processing (NLP) and deep learning methods: LSTM (Long Short Term Memory) and BERT (Bidirectional Encoder Representations from Transformers). As a result of the studies on the Enron e-mail dataset, the BERT model showed 98.93% accuracy and 98.87% f-score performance. The LSTM model showed 97.47% accuracy and f-score performance. With these results, it is seen that the BERT model detects spam with higher accuracy despite the complexity in language structures. © 2024 IEEE.

关键词： Deep learning

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Functional reactive program translator for controlling robot systems

Functional reactive program translator for controlling robot...

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2nd International Conference on Convergent Information Technology, ICCIT 07

作者： Lee, Byeong-Joon Lee, Dong-Ju Woo, Gyun Dept. of Computer Engineering Pusan National University Test Automation Center SureSoft Technologies

ISBN: (纸本)0769530389

FRP (functional reactive programming) is a domain-specific sub-language embedded in Haskell. FRP is developed based on the arrow types and suitable for programming reactive systems such as robot systems. An essential higher-order type, namely signal, provides higher levels of abstractions of reactive systems. However, FRP can not be directly applicable to most robot systems since it is implemented on top of a heavy language Haskell. To overcome this limitation, RT-FRP (real-time functional reactive programming) was proposed, but any practical implementation of RT-FRP has not been proposed currently. In this paper, RT-FRP is implemented as a form of C translator. The proposed translator converts RT-FRP programs into C programs. To measure the effectiveness of the RT-FRP translator, some robot control programs are developed, translated, and loaded on top of LEGO MindStorm. According to the experimental result, the reactive logic can be coded more concisely using RT-FRP than C although the size of the binary code is somewhat increased. © 2007 IEEE.

关键词： Program translators

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Special issue on control of complex systems

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Control Theory and Technology 2016年第4期14卷 261-262页

作者： Hai Lin(1) Cheng Xiang(2) Qiang Ling(3) 1. Dept. Electrical Engineering University of Notre Dame Notre Dame IN 46556 USA 2. Dept. Electrical and Computer Engineering National University of Singapore Singapore 119260 Singapore 3. Dept. Automation University of Science and Technology of China Hefei Anhui 230027 China

Nowadays control systems can find applications in many areas, like aerospace, motion tracking, chemical engineering, physics, biology, economics. To improve control performance is a perpetual goal. With recent progresses of computing technologies, better control performance can be achieved by more judicious control strategies based on more precise and more complicated, such as time-varying, nonlinear, models. Another trend to improve control performance is built upon the divide-and-conquer philosophy, i.e., a complicated control task is cooperatively accomplished by multiple controllers/agents, instead of a single super-powerful controller. This trend is made possible due to great advances in communication, which enable the information exchange among agents and may unite the less powerful agents. The cost for such control performance improvement is higher spatial complexity of control systems.

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