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    Wyświetlanie 1-6 z 6
    Tytuł:
    Performance Analysis of LEACH with Deep Learning in Wireless Sensor Networks
    Autorzy:
    Prajapati, Hardik K.
    Joshi, Rutvij
    Powiązania:
    https://bibliotekanauki.pl/articles/2200710.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    machine learning
    Deep learning
    Convolutional Neural Network (CNN)
    LEACH
    Opis:
    Thousands of low-power micro sensors make up Wireless Sensor Networks, and its principal role is to detect and report specified events to a base station. Due to bounded battery power these nodes are having very limited memory and processing capacity. Since battery replacement or recharge in sensor nodes is nearly impossible, power consumption becomes one of the most important design considerations in WSN. So one of the most important requirements in WSN is to increase battery life and network life time. Seeing as data transmission and reception consume the most energy, it’s critical to develop a routing protocol that addresses the WSN’s major problem. When it comes to sending aggregated data to the sink, hierarchical routing is critical. This research concentrates on a cluster head election system that rotates the cluster head role among nodes with greater energy levels than the others.We used a combination of LEACH and deep learning to extend the network life of the WSN in this study. In this proposed method, cluster head selection has been performed by Convolutional Neural Network (CNN). The comparison has been done between the proposed solution and LEACH, which shows the proposed solution increases the network lifetime and throughput.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 4; 799--805
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A distributed big data analytics model for traffic accidents classification and recognition based on SparkMlLib cores
    Autorzy:
    Mallahi, Imad El
    Riffi, Jamal
    Tairi, Hamid
    Ez-Zahout, Abderrahmane
    Mahraz, Mohamed Adnane
    Powiązania:
    https://bibliotekanauki.pl/articles/27314355.pdf
    Data publikacji:
    2022
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    big data
    machine learning
    traffic accident
    severity prediction
    convolutional neural network
    Opis:
    This paper focuses on the issue of big data analytics for traffic accident prediction based on SparkMllib cores; however, Spark’s Machine Learning Pipelines provide a helpful and suitable API that helps to create and tune classification and prediction models to decision-making concerning traffic accidents. Data scientists have recently focused on classification and prediction techniques for traffic accidents; data analytics techniques for feature extraction have also continued to evolve. Analysis of a huge volume of received data requires considerable processing time. Practically, the implementation of such processes in real-time systems requires a high computation speed. Processing speed plays an important role in traffic accident recognition in real-time systems. It requires the use of modern technologies and fast algorithms that increase the acceleration in extracting the feature parameters from traffic accidents. Problems with overclocking during the digital processing of traffic accidents have yet to be completely resolved. Our proposed model is based on advanced processing by the Spark MlLib core. We call on the real-time data streaming API on spark to continuously gather real-time data from multiple external data sources in the form of data streams. Secondly, the data streams are treated as unbound tables. After this, we call the random forest algorithm continuously to extract the feature parameters from a traffic accident. The use of this proposed method makes it possible to increase the speed factor on processors. Experiment results showed that the proposed method successfully extracts the accident features and achieves a seamless classification performance compared to other conventional traffic accident recognition algorithms. Finally, we share all detected accidents with details onto online applications with other users.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 4; 62--71
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A High-Accuracy of Transmission Line Faults (TLFs) Classification Based on Convolutional Neural Network
    Autorzy:
    Fuada, S.
    Shiddieqy, H. A.
    Adiono, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/1844462.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    fault detection
    fault classification
    transmission lines
    convolutional neural network
    machine learning
    Opis:
    To improve power system reliability, a protection mechanism is highly needed. Early detection can be used to prevent failures in the power transmission line (TL). A classification system method is widely used to protect against false detection as well as assist the decision analysis. Each TL signal has a continuous pattern in which it can be detected and classified by the conventional methods, i.e., wavelet feature extraction and artificial neural network (ANN). However, the accuracy resulting from these mentioned models is relatively low. To overcome this issue, we propose a machine learning-based on Convolutional Neural Network (CNN) for the transmission line faults (TLFs) application. CNN is more suitable for pattern recognition compared to conventional ANN and ANN with Discrete Wavelet Transform (DWT) feature extraction. In this work, we first simulate our proposed model by using Simulink® and Matlab®. This simulation generates a fault signal dataset, which is divided into 45.738 data training and 4.752 data tests. Later, we design the number of machine learning classifiers. Each model classifier is trained by exposing it to the same dataset. The CNN design, with raw input, is determined as an optimal output model from the training process with 100% accuracy.
    Źródło:
    International Journal of Electronics and Telecommunications; 2020, 66, 4; 655-664
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An AI & ML based detection & identification in remote imagery: state-of-the-art
    Autorzy:
    Hashmi, Hina
    Dwivedi, Rakesh
    Kumar, Anil
    Powiązania:
    https://bibliotekanauki.pl/articles/2141786.pdf
    Data publikacji:
    2021
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    convolutional neural network
    remote sensed imagery
    object detection
    artificial intelligence
    feature extraction
    deep learning
    machine learning
    Opis:
    Remotely sensed images and their allied areas of application have been the charm for a long time among researchers. Remote imagery has a vast area in which it is serving and achieving milestones. From the past, after the advent of AL, ML, and DL-based computing, remote imagery is related techniques for processing and analyzing are continuously growing and offering countless services like traffic surveillance, earth observation, land surveying, and other agricultural areas. As Artificial intelligence has become the charm of researchers, machine learning and deep learning have been proven as the most commonly used and highly effective techniques for object detection. AI & ML-based object segmentation & detection makes this area hot and fond to the researchers again with the opportunities of enhanced accuracy in the same. Several researchers have been proposed their works in the form of research papers to highlight the effectiveness of using remotely sensed imagery for commercial purposes. In this article, we have discussed the concept of remote imagery with some preprocessing techniques to extract hidden and fruitful information from them. Deep learning techniques applied by various researchers along with object detection, object recognition are also discussed here. This literature survey is also included a chronological review of work done related to detection and recognition using deep learning techniques.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 4; 3-17
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Restoration of Remote Satellite Sensing Images using Machine and Deep Learning : a Survey
    Autorzy:
    Abdellaoui, Meriem
    Benabdelkader, Souad
    Assas, Ouarda
    Powiązania:
    https://bibliotekanauki.pl/articles/31339413.pdf
    Data publikacji:
    2023
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
    Tematy:
    image restoration
    remote sensing images
    artificial intelligence
    AI
    machine learning
    ML
    deep learning
    DL
    convolutional neural network
    CNN
    Opis:
    Remote sensing satellite images are affected by different types of degradation, which poses an obstacle for remote sensing researchers to ensure a continuous and trouble-free observation of our space. This degradation can reduce the quality of information and its effect on the reliability of remote sensing research. To overcome this phenomenon, the methods of detecting and eliminating this degradation are used, which are the subject of our study. The original aim of this paper is that it proposes a state of art of recent decade (2012-2022) on advances in remote sensing image restoration using machine and deep learning, identified by this survey, including the databases used, the different categories of degradation, as well as the corresponding methods. Machine learning and deep learning based strategies for remote sensing satellite image restoration are recommended to achieve satisfactory improvements.
    Źródło:
    Machine Graphics & Vision; 2023, 32, 2; 147-167
    1230-0535
    2720-250X
    Pojawia się w:
    Machine Graphics & Vision
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Combining Spectral Analysis with Artificial Intelligence in Heart Sound Study
    Autorzy:
    Kucharski, Dariusz
    Kajor, Marcin
    Grochala, Dominik
    Iwaniec, Marek
    Iwaniec, Joanna
    Powiązania:
    https://bibliotekanauki.pl/articles/102508.pdf
    Data publikacji:
    2019
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    deep learning
    heart sound classification
    convolutional neural network
    machine learning
    signal processing
    uczenie głębokie
    klasyfikacja dźwięku serca
    splotowa sieć neuronowa
    uczenie maszynowe
    przetwarzanie sygnałów
    Opis:
    The auscultation technique has been widely used in medicine as a screening examination for ages. Nowadays, advanced electronics and effective computational methods aim to support the healthcare sector by providing dedicated solutions which help physicians and support diagnostic process. In this paper, we propose a machine learning approach for the analysis of heart sounds. We used the spectral analysis of acoustic signal to calculate feature vectors and tested a set of machine learning approaches to provide the most effective detection of cardiac disorders. Finally, we achieved 91% of sensitivity and 99% of positive predictivity for a designed algorithm based on convolutional neural network.
    Źródło:
    Advances in Science and Technology. Research Journal; 2019, 13, 2; 112-118
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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