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    Wyświetlanie 1-8 z 8
    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:
    Skin Lesion Analysis Toward Melanoma Detection Using Deep Learning Techniques
    Autorzy:
    Sherif, Fatma
    Mohamed, Wael A.
    Mohra, A.S.
    Powiązania:
    https://bibliotekanauki.pl/articles/226719.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    melanoma
    skin cancer
    convolutional neural network
    deep learning
    Opis:
    In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set. The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art techniques.
    Źródło:
    International Journal of Electronics and Telecommunications; 2019, 65, 4; 597-602
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On Efficiency of Selected Machine Learning Algorithms for Intrusion Detection in Software Defined Networks
    Autorzy:
    Jankowski, D.
    Amanowicz, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/963945.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    software defined network
    intrusion detection
    machine learning
    Mininet
    SDN
    Opis:
    We propose a concept of using Software Defined Network (SDN) technology and machine learning algorithms for monitoring and detection of malicious activities in the SDN data plane. The statistics and features of network traffic are generated by the native mechanisms of SDN technology.In order to conduct tests and a verification of the concept, it was necessary to obtain a set of network workload test data.We present virtual environment which enables generation of the SDN network traffic.The article examines the efficiency of selected machine learning methods: Self Organizing Maps and Learning Vector Quantization and their enhanced versions.The results are compared with other SDN-based IDS.
    Źródło:
    International Journal of Electronics and Telecommunications; 2016, 62, 3; 247-252
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    QIBMRMN: Design of a Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks
    Autorzy:
    Doorwar, Minaxi
    Malathi, P
    Powiązania:
    https://bibliotekanauki.pl/articles/27311958.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    multimedia
    network
    Q-learning
    GWO
    GA
    Adhoc
    QoS
    iterative
    process
    Opis:
    Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-toperformance ratios. A wide variety of machine learning models are proposed by researchers to perform this task, and most of them are either highly complex, or showcase low-levels of efficiency when applied to large-scale networks. To overcome these issues, this text proposes design of a Q-learning based iterative sleep-scheduling and fuses these schedules with an efficient hybrid bioinspired multipath routing model for largescale multimedia network sets. The proposed model initially uses an iterative Q-Learning technique that analyzes energy consumption patterns of nodes, and incrementally modifies their sleep schedules. These sleep schedules are used by scalar nodes to efficiently wakeup multimedia nodes during adhoc communication requests. These communication requests are processed by a combination of Grey Wolf Optimizer (GWO) & Genetic Algorithm (GA) models, which assist in the identification of optimal paths. These paths are estimated via combined analysis of temporal throughput & packet delivery performance, with node-to-node distance & residual energy metrics. The GWO Model uses instantaneous node & network parameters, while the GA Model analyzes temporal metrics in order to identify optimal routing paths. Both these path sets are fused together via the Q-Learning mechanism, which assists in Iterative Adhoc Path Correction (IAPC), thereby improving the energy efficiency, while reducing communication delay via multipath analysis. Due to a fusion of these models, the proposed Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks (QIBMRMN) is able to reduce communication delay by 2.6%, reduce energy consumed during these communications by 14.0%, while improving throughput by 19.6% & packet delivery performance by 8.3% when compared with standard multimedia routing techniques.
    Źródło:
    International Journal of Electronics and Telecommunications; 2023, 69, 4; 776--784
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    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:
    Deep Learning Can Improve Early Skin Cancer Detection
    Autorzy:
    Mohamed, Abeer
    Mohamed, Wael A.
    Zekry, Abdel Halim
    Powiązania:
    https://bibliotekanauki.pl/articles/963798.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    technology
    dermoscopic lesions
    convolutional
    neural network
    ISIC dataset
    deep learning
    neural networks
    Opis:
    Skin cancer is the most common form of cancer affecting humans. Melanoma is the most dangerous type of skin cancer; and early diagnosis is extremely vital in curing the disease. So far, the human knowledge in this field is very limited, thus, developing a mechanism capable of identifying the disease early on can save lives, reduce intervention and cut unnecessary costs. In this paper, the researchers developed a new learning technique to classify skin lesions, with the purpose of observing and identifying the presence of melanoma. This new technique is based on a convolutional neural network solution with multiple configurations; where the researchers employed an International Skin Imaging Collaboration (ISIC) dataset. Optimal results are achieved through a convolutional neural network composed of 14 layers. This proposed system can successfully and reliably predict the correct classification of dermoscopic lesions with 97.78% accuracy.
    Źródło:
    International Journal of Electronics and Telecommunications; 2019, 65, 3; 507-512
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Wireless Sensor Node Localization based on LNSM and Hybrid TLBO : Unilateral technique for Outdoor Location
    Autorzy:
    Kaundal, V.
    Sharma, P.
    Prateek, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/226010.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    log normal shadowing model (LNSM)
    teacher learning based optimization (TLBO)
    trilateration
    unilateral
    RSSI
    ZigBee
    wireless sensor network
    Opis:
    The paper aims at localization of the anchor node (fixed node) by pursuit nodes (movable node) in outdoor location. Two methods are studied for node localization. The first method is based on LNSM (Log Normal Shadowing Model) technique to localize the anchor node and the second method is based on Hybrid TLBO (Teacher Learning Based Optimization Algorithm) - Unilateral technique. In the first approach the ZigBee protocol has been used to localize the node, which uses RSSI (Received Signal Strength Indicator) values in dBm. LNSM technique is implemented in the self-designed hardware node and localization is studied for Outdoor location. The statistical analysis using RMSE (root mean square error) for outdoor location is done and distance error found to be 35 mtrs. The same outdoor location has been used and statistical analysis is done for localization of nodes using Hybrid TLBO-Unilateral technique. The Hybrid-TLBO Unilateral technique significantly localizes anchor node with distance error of 0.7 mtrs. The RSSI values obtained are normally distributed and standard deviation in RSSI value is observed as 1.01 for outdoor location. The node becomes 100% discoverable after using hybrid TLBO- Unilateral technique.
    Źródło:
    International Journal of Electronics and Telecommunications; 2017, 63, 4; 389-397
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Development of Speaker Voice Identification Using Main Tone Boundary Statistics for Applying To Robot-Verbal Systems
    Autorzy:
    Amirgaliyev, Yedilkhan
    Musabayev, Timur
    Yedilkhan, Didar
    Wojcik, Waldemar
    Amirgaliyeva, Zhazira
    Powiązania:
    https://bibliotekanauki.pl/articles/963938.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    speaker voice identification
    voice interface (FXO)
    human being
    human-robot interaction
    HRI
    speech recognition
    statistics of voice fundamental tone
    computer-aided learning
    neural network
    Opis:
    Hereby there is given the speaker identification basic system. There is discussed application and usage of the voice interfaces, in particular, speaker voice identification upon robot and human being communication. There is given description of the information system for speaker automatic identification according to the voice to apply to robotic-verbal systems. There is carried out review of algorithms and computer-aided learning libraries and selected the most appropriate, according to the necessary criteria, ALGLIB. There is conducted the research of identification model operation performance assessment at different set of the fundamental voice tone. As the criterion of accuracy there has been used the percentage of improperly classified cases of a speaker identification.
    Źródło:
    International Journal of Electronics and Telecommunications; 2020, 66, 3; 583-588
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-8 z 8

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