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    Wyświetlanie 1-6 z 6
    Tytuł:
    Adaptive Rider Feedback Artificial Tree Optimization-Based Deep Neuro-Fuzzy Network for Classification of Sentiment Grade
    Autorzy:
    Jasti, Sireesha
    Kumar, G.V.S. Raj
    Powiązania:
    https://bibliotekanauki.pl/articles/2200961.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    deep learning network
    feedback artificial tree
    natural language processing (NLP)
    rider optimization algorithm
    sentiment grade classification
    Opis:
    Sentiment analysis is an efficient technique for expressing users’ opinions (neutral, negative or positive) regarding specific services or products. One of the important benefits of analyzing sentiment is in appraising the comments that users provide or service providers or services. In this work, a solution known as adaptive rider feedback artificial tree optimization-based deep neuro-fuzzy network (RFATO-based DNFN) is implemented for efficient sentiment grade classification. Here, the input is pre-processed by employing the process of stemming and stop word removal. Then, important factors, e.g. SentiWordNet-based features, such as the mean value, variance, as well as kurtosis, spam word-based features, term frequency-inverse document frequency (TF-IDF) features and emoticon-based features, are extracted. In addition, angular similarity and the decision tree model are employed for grouping the reviewed data into specific sets. Next, the deep neuro-fuzzy network (DNFN) classifier is used to classify the sentiment grade. The proposed adaptive rider feedback artificial tree optimization (A-RFATO) approach is utilized for the training of DNFN. The A-RFATO technique is a combination of the feedback artificial tree (FAT) approach and the rider optimization algorithm (ROA) with an adaptive concept. The effectiveness of the proposed A-RFATO-based DNFN model is evaluated based on such metrics as sensitivity, accuracy, specificity, and precision. The sentiment grade classification method developed achieves better sensitivity, accuracy, specificity, and precision rates when compared with existing approaches based on Large Movie Review Dataset, Datafiniti Product Database, and Amazon reviews.
    Źródło:
    Journal of Telecommunications and Information Technology; 2023, 1; 37--50
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Network Traffic Classification in an NFV Environment using Supervised ML Algorithms
    Autorzy:
    Ilievski, Gjorgji
    Latkoski, Pero
    Powiązania:
    https://bibliotekanauki.pl/articles/1839335.pdf
    Data publikacji:
    2021
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    classification
    machine learning
    network functions virtualization
    network traffic
    Opis:
    We have conducted research on the performance of six supervised machine learning (ML) algorithms used for network traffic classification in a virtual environment driven by network function virtualization (NFV). The performance-related analysis focused on the precision of the classification process, but also in time-intensity (speed) of the supervised ML algorithms. We devised specific traffic taxonomy using commonly used categories, with particular emphasis placed on VoIP and encrypted VoIP protocols serve as a basis of the 5G architecture. NFV is considered to be one of the foundations of 5G development, as the traditional networking components are fully virtualized, in many cases relaying on mixed cloud solutions, both of the premise- and public cloud-based variety. Virtual machines are being replaced by containers and application functions while most of the network traffic is flowing in the east-west direction within the cloud. The analysis performed has shown that in such an environment, the Decision Tree algorithm is best suited, among the six algorithms considered, for performing classification-related tasks, and offers the required speed that will introduce minimal delays in network flows, which is crucial in 5G networks, where packet delay requirements are of great significance. It has proven to be reliable and offered excellent overall performance across multiple network packet classes within a virtualized NFV network architecture. While performing the classification procedure, we were working only with the statistical network flow features, leaving out packet payload, source, destination- and port-related information, thus making the analysis valid not only from the technical, but also from the regulatory point of view.
    Źródło:
    Journal of Telecommunications and Information Technology; 2021, 3; 23-31
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intrusion Detection in Software Defined Networks with Self-organized Maps
    Autorzy:
    Jankowski, D.
    Amanowicz, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/308109.pdf
    Data publikacji:
    2015
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    IDS dataset
    machine learning
    metasploit
    network security
    network simulation
    OpenFlow
    virtualization
    Opis:
    The Software Defined Network (SDN) architecture provides new opportunities to implement security mechanisms in terms of unauthorized activities detection. At the same time, there are certain risks associated with this technology. The presented approach covers a conception of the measurement method, virtual testbed and classification mechanism for SDNs. The paper presents a measurement method which allows collecting network traffic flow parameters, generated by a virtual SDN environment. The collected dataset can be used in machine learning methods to detect unauthorized activities.
    Źródło:
    Journal of Telecommunications and Information Technology; 2015, 4; 3-9
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning
    Autorzy:
    Ganum, Adriana
    Iskandar, D. N. F. Awang
    Chin, Lim Phei
    Fauzi, Ahmad Hadinata
    Powiązania:
    https://bibliotekanauki.pl/articles/2058502.pdf
    Data publikacji:
    2022
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    automated optical inspection
    machine learning
    neural network
    wafer imperfection identification
    Opis:
    Defect detection is an important step in industrial production of monocrystalline silicon. Through the study of deep learning, this work proposes a framework for classifying monocrystalline silicon wafer defects using deep transfer learning (DTL). An existing pre-trained deep learning model was used as the starting point for building a new model. We studied the use of DTL and the potential adaptation of Mo bileNetV2 that was pre-trained using ImageNet for extracting monocrystalline silicon wafer defect features. This has led to speeding up the training process and to improving performance of the DTL-MobileNetV2 model in detecting and classifying six types of monocrystalline silicon wafer defects (crack, double contrast, hole, microcrack, saw-mark and stain). The process of training the DTL-MobileNetV2 model was optimized by relying on the dense block layer and global average pooling (GAP) method which had accelerated the convergence rate and improved generalization of the classification network. The monocrystalline silicon wafer defect classification technique relying on the DTL-MobileNetV2 model achieved the accuracy rate of 98.99% when evaluated against the testing set. This shows that DTL is an effective way of detecting different types of defects in monocrystalline silicon wafers, thus being suitable for minimizing misclassification and maximizing the overall production capacities.
    Źródło:
    Journal of Telecommunications and Information Technology; 2022, 1; 34--42
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multicriteria Oppositional-Learnt Dragonfly Resource-Optimized QoS Driven Channel Selection for CRNs
    Autorzy:
    Sirisha Devi, Ch. S. N.
    Maloj, Suman
    Powiązania:
    https://bibliotekanauki.pl/articles/2174446.pdf
    Data publikacji:
    2022
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    cognitive ratio network (CRN)
    multicriteria dragonfly optimization
    oppositional learning
    optimal available channel
    QoS metric
    Opis:
    Cognitive radio networks (CRNs) allow their users to achieve adequate QoS while communicating. The major concern related to CRN is linked to guaranteeing free channel selection to secondary users (SUs) in order to maintain the network’s throughput. Many techniques have been designed in the literature for channel selection in CRNs, but the throughput of the network has not been enhanced yet. Here, an efficient technique, known as multicriteria oppositional-learnt dragonfly resourceoptimized QoS-driven channel selection (MOLDRO-QoSDCS) is proposed to select the best available channel with the expected QoS metrics. The MOLDRO-QoSDCS technique is designed to improve energy efficiency and throughput, simultaneously reducing the sensing time. By relying on oppositional-learnt multiobjective dragonfly optimization, the optimal available channel is selected depending on signal-to-noise ratio, power consumption, and spectrum utilization. In the optimization process, the population of the available channels is initialized. Then, using multiple criteria, the fitness function is determined and the available channel with the best resource availability is selected. Using the selected optimal channel, data transmission is effectively performed to increase the network’s throughput and to minimize the sensing time. The simulated outputs obtained with the use of Matlab are compared with conventional algorithms in order to verify the performance of the solution. The MOLDRO-QoSDCS technique performs better than other methods in terms of throughput, sensing time, and energy efficiency.
    Źródło:
    Journal of Telecommunications and Information Technology; 2022, 4; 41--46
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Designing Smart Antennas Using Machine Learning Algorithms
    Autorzy:
    Samantaray, Barsa
    Das, Kunal Kumar
    Roy, Jibendu Sekhar
    Powiązania:
    https://bibliotekanauki.pl/articles/27312957.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    artificial neural network
    decision tree
    ensemble algorithm
    machine learning
    smart antenna
    support vector machine
    Opis:
    Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.
    Źródło:
    Journal of Telecommunications and Information Technology; 2023, 4; 46--52
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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