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  • 1
    Online Resource
    Online Resource
    Social Touch Gesture Recognition Using Convolutional Neural Network
    Hindawi Limited ; 2018
    In:  Computational Intelligence and Neuroscience Vol. 2018 ( 2018-10-08), p. 1-10
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2018 ( 2018-10-08), p. 1-10
    Abstract: Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.
    Type of Medium: Online Resource
    ISSN: 1687-5265 , 1687-5273
    URL: Article
    DOI: 10.1155/2018/6973103
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2018
    detail.hit.zdb_id: 2388208-6
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  • 2
    Online Resource
    Online Resource
    Skin cancer classification dermatologist-level based on deep learning model
    Universidade Estadual de Maringa ; 2022
    In:  Acta Scientiarum. Technology Vol. 45 ( 2022-12-19), p. e61531-
    Details
    In: Acta Scientiarum. Technology, Universidade Estadual de Maringa, Vol. 45 ( 2022-12-19), p. e61531-
    Abstract: Medical image analysis is a significant burden for doctors, therefore, it is used to supplement image processing. Many medical images are assumed to be diagnosed as accurately as healthcare experts when the precision of image detection and recognition in an image processing approach matches that of a human being. Artificial Intelligence (AI) based predictive modelling is an important component of many healthcare solutions. This paper develops and implements a neural network-based method for skin cancer prediction to expose the neural network's strength in this field. This method determines which form of deep learning is best for diagnosing diseases with an accuracy exceeds human ability in terms of speed and accuracy, and determines the optimum number of layers and neurons in each layer for both Convolutional Neural network (CNN) and Deep Neural Network (DNN) to obtain the best possible precision. The results of the proposed method showed impressive results, especially for CNN. There is a clear superiority of CNN over DNN. The CNN (which relies on convolution filters) provides great results in extracting features due to the focus on the intended area of the image without the surrounding area region of interest. This led to a remarkable decrease in the number of parameters and the speed of attaining results with higher accuracy. The results indicated that CNN has a high accuracy rate compared with the other existing methods where the accuracy rate of CNN is 98.5%.
    Type of Medium: Online Resource
    ISSN: 1806-2563 , 1807-8664
    URL: Article
    DOI: 10.4025/actascitechnol.v45i1.61531
    Language: Unknown
    Publisher: Universidade Estadual de Maringa
    Publication Date: 2022
    detail.hit.zdb_id: 2449424-0
    SSG: 7,36
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  • 3
    Online Resource
    Online Resource
    An Inclusive Survey of Machine Learning based Hand Gestures Recognition Systems in Recent Applications
    IOP Publishing ; 2021
    In:  IOP Conference Series: Materials Science and Engineering Vol. 1076, No. 1 ( 2021-02-01), p. 012047-
    Details
    In: IOP Conference Series: Materials Science and Engineering, IOP Publishing, Vol. 1076, No. 1 ( 2021-02-01), p. 012047-
    Abstract: Hand gestures represent one of the most prevalent types of body language which can be utilized for interaction and communication. Although the other types of body language represent a more general state of emotional, hand gestures capable of possessing specified linguistic content inside it. Because of the expressiveness and speed in interaction, hand gestures are commonly utilized in human-computer interaction systems (HCI), sign languages, virtual reality, and gaming. In the process of recognizing hand gestures, the complexity and diversity of gestures will extremely impact on the recognition rate and reliability. The existence of machine learning techniques can be effectively exploited in the task of improving the rate of hand gesture recognition. This paper inspected the performance of machine learning techniques in recognizing vision and sensors based hand gestures in the recently existing applications. Additionally, the widely used architecture applied in various datasets has been considered which includes the acquisition of data, pre-processing, the extraction of features, and classification.
    Type of Medium: Online Resource
    ISSN: 1757-8981 , 1757-899X
    URL: Article
    DOI: 10.1088/1757-899X/1076/1/012047
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 2506501-4
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  • 4
    Online Resource
    Online Resource
    MRI image segmentation using machine learning networks and level set approaches
    Institute of Advanced Engineering and Science ; 2022
    In:  International Journal of Electrical and Computer Engineering (IJECE) Vol. 12, No. 1 ( 2022-02-01), p. 793-
    Details
    In: International Journal of Electrical and Computer Engineering (IJECE), Institute of Advanced Engineering and Science, Vol. 12, No. 1 ( 2022-02-01), p. 793-
    Abstract: 〈 span 〉 The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones. 〈 /span 〉
    Type of Medium: Online Resource
    ISSN: 2722-2578 , 2088-8708
    URL: Issue
    URL: Article
    DOI: 10.11591/ijece.v12i1
    DOI: 10.11591/ijece.v12i1.pp793-801
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2022
    detail.hit.zdb_id: 2667127-X
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  • 5
    Online Resource
    Online Resource
    A novel algorithm for estimation of Twitter users location using public available information
    Walter de Gruyter GmbH ; 2020
    In:  International Journal on Smart Sensing and Intelligent Systems Vol. 13, No. 1 ( 2020-01-01), p. 1-10
    Details
    In: International Journal on Smart Sensing and Intelligent Systems, Walter de Gruyter GmbH, Vol. 13, No. 1 ( 2020-01-01), p. 1-10
    Abstract: Social media networks are an attractive and hot research area in the big data community because of their numerous active users. One of the most widely studied topics in social networks is the prediction from the public available data. Recently, researchers have successfully predicted many statistical and human properties from social media networks using different machine learning algorithms. In this paper, a new efficient and accurate algorithm is proposed to predict the country location of a Twitter user using his or her public information only. The proposed algorithm employs the public information of the Twitter user and that of his or her followers and friends to predict his or her location without using GPS data. A convenient data set of Twitter users is gathered and used to test our proposed algorithm using KNIME software. The proposed algorithm is compared with other state-of-the-art algorithms, and results showed that our proposed algorithm significantly outperforms other location detection algorithms by using Twitter users from different countries.
    Type of Medium: Online Resource
    ISSN: 1178-5608
    URL: Article
    DOI: 10.21307/ijssis-2020-012
    Language: English
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2519374-0
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  • 6
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    Online Resource
    Efficient Routing in VANETs Using MRRP Algorithm
    University of Diyala, College of Science ; 2023
    In:  Diyala Journal of Engineering Sciences ( 2023-09-03), p. 134-146
    Details
    In: Diyala Journal of Engineering Sciences, University of Diyala, College of Science, ( 2023-09-03), p. 134-146
    Abstract: Designing a reliable routing protocol for Vehicular Ad hoc Network (VANET) poses considerable challenges due to certain unique challenges inherently present in Vehicular Ad hoc Network (VANET) topology. Some of them are needed for vehicles acting as nodes having to abide by traffic rules, uncertain inter-vehicular speed variations that may affect link stability etc. Designing a routing protocol capable of dealing with multiple limiting conditions such as long congestion periods, link failures and handoffs is a challenging task, where most of the existing multipath routing protocol shows poor performance. In this paper, the proposed Multipath Route Restoration Protocol (MRRP)is aimed at providing a robust communication channel in case of link failure between nodes. This is realized by focusing on better route maintenance for the protocol. In a wireless network, a routing protocol determines the particular ways in which routers connect. In a wireless network, as the number of hops in a wireless communication path increases, various signal factors such as interference and path loss degrade the network performance. however, sending data over a longer distance will reduce throughput. Furthermore, link stability is substantially impacted by the unpredictable movement of vehicles. Multipath routing is regarded as a potential solution to improve packet delivery and end-to-end delay in VANETs.
    Type of Medium: Online Resource
    ISSN: 2616-6909 , 1999-8716
    URL: Article
    DOI: 10.24237/djes.2023.16311
    Language: Unknown
    Publisher: University of Diyala, College of Science
    Publication Date: 2023
    detail.hit.zdb_id: 2743730-9
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  • 7
    Online Resource
    Online Resource
    Efficient FIR Filter Architecture using FPGA
    Bentham Science Publishers Ltd. ; 2020
    In:  Recent Advances in Computer Science and Communications Vol. 13, No. 1 ( 2020-03-13), p. 91-98
    Details
    In: Recent Advances in Computer Science and Communications, Bentham Science Publishers Ltd., Vol. 13, No. 1 ( 2020-03-13), p. 91-98
    Abstract: Advance communication systems require new techniques for FIR filters with resource efficiency in terms of high performance and low power consumption. Lowcomplexity architectures are required by FIR filters for implementation in field programmable gate Arrays (FPGA). In addition, FIR filters in multistandard wireless communication systems must have low complexity and be reconfigurable. The coefficient multipliers of FIR filters are complicated. Objective: The implementation and application of high tap FIR filters by a partial product reduce this complexity. Thus, this article proposes a novel digital finite impulse response (FIR) filter architecture with FPGA. Method: The proposed technique FIR filter is based on a new architecture method and implemented using the Quartus II design suite manufactured by Altera. Also, the proposed architecture is coded in Verilog HDL and the code developed from the proposed architecture has been simulated using Modelsim. This efficient FIR filter architecture is based on the shift and add method. Efficient circuit techniques are used to further improve power and performance. In addition, the proposed architecture achieves better hardware requirements as multipliers are reduced. A 10-tap FIR filter is implemented on the proposed architecture. Results: The design’s example demonstrates a 25% reduction in resource usage compared to existing reconfigurable architectures with FPGA synthesis. In addition, the speed of the proposed architecture is 37% faster than the best performance of existing methods. Conclusion: The proposed architecture offers low power and improved speed with the lowcomplexity design that gives the best architecture FIR filter for both reconfigurable and fixed applications.
    Type of Medium: Online Resource
    ISSN: 2666-2558
    URL: Article
    DOI: 10.2174/2213275912666190603115506
    Language: English
    Publisher: Bentham Science Publishers Ltd.
    Publication Date: 2020
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  • 8
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    Online Resource
    A Systematic Mapping Study on Radio Frequency Identification Security
    Bentham Science Publishers Ltd. ; 2021
    In:  International Journal of Sensors, Wireless Communications and Control Vol. 10, No. 5 ( 2021-02-09), p. 659-668
    Details
    In: International Journal of Sensors, Wireless Communications and Control, Bentham Science Publishers Ltd., Vol. 10, No. 5 ( 2021-02-09), p. 659-668
    Abstract: Radio frequency identification (RFID) technology can be applied in identification, security and tracking system platforms due to its flexible and low-cost implementation. The sensing ability of this technology can also be used to monitor real-life environmental changes and physical phenomena. RFID is designed to work in open wireless communication. Therefore, this system can be attacked by different malware. Enhancing RFID with new security and privacy features isimportant at present. Objective: The current work is a systematic mapping study on RFID security. The types, contributions, facets and activities of research on RFID security were plotted. Methods: The systematic mapping for a specific search are was done by identifying the number of RQs. These RQs help researchers obtain comprehensive related studies. The RQs must be selected carefully because they determine the research direction and help rapidly obtain the required information and studies. Results: We gathered 2133 relevant studies and retained 92 primary papers after four filtering processes. We classified them into three facets. Results provide researchers and readers an overview of existing relevant studies and help them identify the properties in the focus area. Conclusion: The systematic mapping study used to report the design, execution, tool, application and results of a Radio frequency identification (RFID). We systematically chose and deeply analyzed all concepts related (RFID) techniques and implementation methods which provide a complete about the (RFID) state and environment of studies.
    Type of Medium: Online Resource
    ISSN: 2210-3279
    URL: Article
    DOI: 10.2174/2210327909666191004095834
    Language: English
    Publisher: Bentham Science Publishers Ltd.
    Publication Date: 2021
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  • 9
    Online Resource
    Online Resource
    A hybrid approach towards movie recommendation system with collaborative filtering and association rule mining
    Universidade Estadual de Maringa ; 2022
    In:  Acta Scientiarum. Technology Vol. 44 ( 2022-03-11), p. e58925-
    Details
    In: Acta Scientiarum. Technology, Universidade Estadual de Maringa, Vol. 44 ( 2022-03-11), p. e58925-
    Abstract: There is a huge information stockpile available on the internet. But the available information still throws a stiff challenge to users while selecting the needed information. Such an issue can be solved by applying information filtering for locating the required information through a Recommender System. While using a RS, the users find it easy to curate and collect relevant information out of massive databanks. Though various types of RS are currently available, yet the RS developed by Collaborative Filtering techniques has proven to be the most suitable for many problems. Among the various Recommended Systems available, movie recommendation system is the most widely used one.  In this system, the recommendations will be made based on the similarities in the characteristics as exhibited by users / items. The movie recommendation system contains a huge list of user objects and item objects. This paper combines Collaborative Filtering Technique with association rules mining for better compatibility and assurance while delivering better recommendations. Hence, in the process, the produced recommendations can be considered as strong recommendations. The hybridization involving both collaborative filtering and association rules mining can provide strong, high-quality recommendations, even when enough data is unavailable. This article combines various recommendations for creating a movie recommendation system by using common filtering techniques and data mining techniques
    Type of Medium: Online Resource
    ISSN: 1806-2563 , 1807-8664
    URL: Article
    DOI: 10.4025/actascitechnol.v44i1.58925
    Language: Unknown
    Publisher: Universidade Estadual de Maringa
    Publication Date: 2022
    detail.hit.zdb_id: 2449424-0
    SSG: 7,36
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  • 10
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    Online Resource
    An Effective Deep Learning Model to Discriminate Coronavirus Disease From Typical Pneumonia
    IGI Global ; 2023
    In:  International Journal of Service Science, Management, Engineering, and Technology Vol. 13, No. 1 ( 2023-3-17), p. 1-16
    Details
    In: International Journal of Service Science, Management, Engineering, and Technology, IGI Global, Vol. 13, No. 1 ( 2023-3-17), p. 1-16
    Type of Medium: Online Resource
    ISSN: 1947-959X , 1947-9603
    URL: Issue
    URL: Issue
    URL: Journal
    URL: Article
    DOI: 10.4018/IJSSMET.2022
    DOI: 10.4018/IJSSMET.20220101
    DOI: 10.4018/IJSSMET
    DOI: 10.4018/IJSSMET.313175
    Language: Ndonga
    Publisher: IGI Global
    Publication Date: 2023
    detail.hit.zdb_id: 2586972-3
    SSG: 3,2
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