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    Wyświetlanie 1-4 z 4
    Tytuł:
    Application of the spherical fuzzy dematel model for assessing the drone apps issues
    Autorzy:
    Pandey, Mamta
    Litoriya, Ratnesh
    Pandey, Prateek
    Powiązania:
    https://bibliotekanauki.pl/articles/27314238.pdf
    Data publikacji:
    2023
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    drone app
    issues
    multi-criteria decision making
    spherical fuzzy DEMATEL
    Opis:
    During the past few years, the number of drones (unmanned aerial vehicles, or UAVs) manufactured and purchased has risen dramatically. It is predicted that it will continue to spread, making its use inevitable in all walks of life. Drone apps are therefore expected to overrun the app stores in the near future. The UAV’s software is not being studied/researched despite several active research and studies being carried out in the UAV’s hardware field. A large‐scale empirical analysis of Google Play Store Platform apps connected to drones is being done in this direction. There are, however, a number of challenges with drone apps because of the lack of formal and specialized app development procedures. In this paper, eleven drone app issues have been identified. Then we applied the DEMATEL (Decision Making Trial and Evaluation Laboratory) method to analyze the drone app issues (DIs) and divide these issues into cause and effect groups. First, multiple experts assess the direct relationships between influential issues in drone apps. The evaluation results are presented in spherical fuzzy numbers (SFN). Secondly, we convert the linguistic terms into SFN. Thirdly, based on DEMATEL, the cause‐effect classifications of issues are obtained. Finally, the issues in the cause category are identified as DI’s in drone apps. The outcome of the research is compared with the other variants of DEMATEL, like rough‐Z‐number‐ based DEMATEL and spherical fuzzy number, and the comparative results suggest that spherical fuzzy DEMA‐ TEL is the most fitting method to analyze the interrela‐ tionship of different issues in drone apps. The findings revealed that highest influenced values feature request (DI9 ) 3.12, Customer support (DI6) 2.91, Connection/Sync ((DI4) 2./72, Cellular Data Usage ((DI3) 2.51, Battery (DI2) 2.31, Advertisements ((DI1) – 0.3, Cost (DI5) – 0.5, Additional cost (D11) – 0.5, Device Compatibility (DI7) – 0.96, and Functional Error (DI10) – 1.2. The outcome of this work definitely assists the software industry in the successful identification of the critical issues where professionals and project managers could really focus.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2023, 17, 2; 36--50
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Integrated and deep learning–based social surveillance system : a novel approach
    Autorzy:
    Litoriya, Ratnesh
    Ramchandani, Dev
    Moyal, Dhruvansh
    Bothra, Dhruv
    Powiązania:
    https://bibliotekanauki.pl/articles/27314204.pdf
    Data publikacji:
    2022
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    Video Surveillance
    object detection
    object tracking
    YOLO v4 algorithm
    OpenCV
    Opis:
    In industry and research, big data applications are gaining a lot of traction and space. Surveillance videos contribute significantly to big unlabelled data. The aim of visual surveillance is to understand and determine object behavior. It includes static and moving object detection, as well as video tracking to comprehend scene events. Object detection algorithms may be used to identify items in any video scene. Any video surveillance system faces a significant challenge in detecting moving objects and differentiating between objects with same shapes or features. The primary goal of this work is to provide an integrated framework for quick overview of video analysis utilizing deep learning algorithms to detect suspicious activity. In greater applications, the detection method is utilized to determine the region where items are available and the form of objects in each frame. This video analysis also aids in the attainment of security. Security may be characterized in a variety of ways, such as identifying theft or violation of covid protocols. The obtained results are encouraging and superior to existing solutions with 97% accuracy.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 3; 30--39
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A cryptographic security mechanism for dynamic groups for public cloud environments
    Autorzy:
    Malviya, Sheenal
    Dave, Sourabh
    Bandhu, Kailash Chandra
    Litoriya, Ratnesh
    Powiązania:
    https://bibliotekanauki.pl/articles/27314248.pdf
    Data publikacji:
    2022
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    cloud computing
    cloud data security
    dynamic groups
    homomorphic encryption
    chaotic network
    throughput
    avalanche effect
    Opis:
    Cloud computing has emerged as a significant technology domain, primarily due to the emergence of big data, machine learning, and quantum computing applications. While earlier, cloud computing services were focused mainly on providing storage and some infrastructures/ platforms for applications, the need to advance computational power analysis of massive datasets. It has made cloud computing almost inevitable from most client-based applications, mobile applications, or web applications. The allied challenge to protect data shared from and to cloud-based platforms has cropped up with the necessity to access public clouds. While conventional cryptographic algorithms have been used for securing and authenticating cloud data, advancements in cryptanalysis and access to faster computation have led to possible threats to the traditional security of cloud mechanisms. This has led to extensive research in homomorphic encryption pertaining to cloud security. In this paper, a security mechanism is designed targeted towards dynamic groups using public clouds. Cloud security mechanisms generally face a significant challenge in terms of overhead, throughput, and execution time to encrypt data from dynamic groups with frequent member addition and removal. A two-stage homomorphic encryption process is proposed for data security in this paper. The performance of the proposed system is evaluated in terms of the salient cryptographic metrics, which are the avalanche effect, throughput, and execution time. A comparative analysis with conventional cryptographic algorithms shows that the proposed system outperforms them regarding the cryptographic performance metrics.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 2; 46--54
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Real-time face mask detection in mass gatherings to reduce Covid-19 spread
    Autorzy:
    Soner, Swapnil
    Litoriya, Ratnesh
    Khatri, Ravi
    Hussain, Ali Asgar
    Pagrey, Shreyas
    Kushwaha, Sunil Kumar
    Powiązania:
    https://bibliotekanauki.pl/articles/27314233.pdf
    Data publikacji:
    2023
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    Covid
    machine learning
    face mask detection
    Deep Learning
    Opis:
    The Covid 19 (coronavirus) pandemic has become one of the most lethal health crises worldwide. This virus gets transmitted from a person by respiratory droplets when they sneeze or when they speak. According to leading and well‐known scientists, wearing face masks and maintain‐ ing six feet of social distance are the most substantial protections to limit the virus’s spread. In the proposed model we have used the Convolutional Neural Network (CNN) algorithm of Deep Learning (DL) to ensure efficient real‐time mask detection. We have divided the system into two parts—1. Train Face Mask Detector 2. Apply Face Mask Detector—for better understanding. This is a real‐ time application that is used to discover or detect the person who is wearing a mask at the proper position or not, with the help of camera detection. The system has achieved an accuracy of 99% after being trained with the dataset, which contains around 1376 images of width and height 224×224 and also gives the alarm beep message after the detection of no mask or improper mask usage in a public place.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2023, 17, 1; 51--58
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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