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Tytuł:
A deep hybrid model for human-computer interaction using dynamic hand gesture recognition
Autorzy:
Ramalingam, Brindha
Angappan, Geetha
Powiązania:
https://bibliotekanauki.pl/articles/38702766.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
dynamic hand gesture
human-computer interaction
long short-term memory
convolutional neural network
dynamiczny gest ręki
interakcja człowiek-komputer
pamięć krótkotrwała
konwolucyjna sieć neuronowa
Opis:
Dynamic hand gestures attract great interest and are utilized in different fields. Amongthese, man-machine interaction is an interesting area that makes use of the hand to providea natural way of interaction between them. A dynamic hand gesture recognition system isproposed in this paper, which helps to perform control operations in applications such asmusic players, video games, etc. The key motivation of this research is to provide a simple, touch-free system for effortless and faster human-computer interaction (HCI). As thisproposed model employs dynamic hand gestures, HCI is achieved by building a modelwith a convolutional neural network (CNN) and long short-term memory (LSTM) net-works. CNN helps in extracting important features from the images and LSTM helpsto extract the motion information between the frames. Various models are constructedby differing the LSTM and CNN layers. The proposed system is tested on an existing EgoGesture dataset that has several classes of gestures from which the dynamic gesturesare utilized. This dataset is used as it has more data with a complex background, actionsperformed with varying speeds, lighting conditions, etc. This proposed hand gesture recognition system attained an accuracy of 93%, which is better than other existing systemssubject to certain limitations.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 3; 263-276
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Neural Network Model for Object Mask Detection in Medical Images
Autorzy:
Tereikovskyi, Igor
Korchenko, Oleksander
Bushuyev, Sergey
Tereikovskyi, Oleh
Ziubina, Ruslan
Veselska, Olga
Powiązania:
https://bibliotekanauki.pl/articles/2200721.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
model
neural network
object mask
medical images
Opis:
In modern conditions in the field of medicine, raster image analysis systems are becoming more widespread, which allow automating the process of establishing a diagnosis based on the results of instrumental monitoring of a patient. One of the most important stages of such an analysis is the detection of the mask of the object to be recognized on the image. It is shown that under the conditions of a multivariate and multifactorial task of analyzing medical images, the most promising are neural network tools for extracting masks. It has also been determined that the known detection tools are highly specialized and not sufficiently adapted to the variability of the conditions of use, which necessitates the construction of an effective neural network model adapted to the definition of a mask on medical images. An approach is proposed to determine the most effective type of neural network model, which provides for expert evaluation of the effectiveness of acceptable types of models and conducting computer experiments to make a final decision. It is shown that to evaluate the effectiveness of a neural network model, it is possible to use the Intersection over Union and Dice Loss metrics. The proposed solutions were verified by isolating the brachial plexus of nerve fibers on grayscale images presented in the public Ultrasound Nerve Segmentation database. The expediency of using neural network models U-Net, YOLOv4 and PSPNet was determined by expert evaluation, and with the help of computer experiments, it was proved that U-Net is the most effective in terms of Intersection over Union and Dice Loss, which provides a detection accuracy of about 0.89. Also, the analysis of the results of the experiments showed the need to improve the mathematical apparatus, which is used to calculate the mask detection indicators.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 1; 41--46
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy
Autorzy:
Alemam, Mustafa K.
Yong, Bin
Sani-Mohammed, Abubakar
Powiązania:
https://bibliotekanauki.pl/articles/27314479.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Centrum Badań Kosmicznych PAN
Tematy:
digital elevation model
GIS
artificial neural network
interpolation methods
SRTM
Opis:
The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.
Źródło:
Artificial Satellites. Journal of Planetary Geodesy; 2023, 58, 3; 122--170
2083-6104
Pojawia się w:
Artificial Satellites. Journal of Planetary Geodesy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applications of generative models with a latent observation subspace in vibrodiagnostics
Autorzy:
Puchalski, Andrzej
Komorska, Iwona
Powiązania:
https://bibliotekanauki.pl/articles/27313835.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
vibration signal
deep neural network
generative adversarial network
GAN model
synthetic subspace
sygnał wibracyjny
głęboka sieć neuronowa
GAN
wibrodiagnostyka
Opis:
The vibration signal is one of the most essential diagnostic signals, the analysis of which allows for determining the dynamic state of the monitored machine set. In the era of cyber-physical industrial systems, making diagnostic decisions involves the study of large databases from previous registers and data downloaded from machines in real-time. However, the recorded signals mainly concern the operational status of the monitored object. Insufficient training data regarding failure states hinders the operation of classification algorithms. Progress in machine learning has created a new avenue for the advancement of diagnostic methods based on models. These methods now have the capability to produce signals through random sampling from a hidden space or generate fresh instances of input data from noise. The article suggests the use of a Generative Adversarial Network (GAN) model as a tool to create synthetic measurement observations for vibration monitoring. The effectiveness of the synthetic data generation algorithm was verified on the example of the vibration signal recorded during tests of the drive system of a motor vehicle.
Źródło:
Diagnostyka; 2023, 24, 4; art. no. 2023413
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of an Information Security System Based on Modeling Distributed Computer Network Vulnerability Indicators of an Informatization Object
Autorzy:
Lakhno, Valerii
Alimseitova, Zhuldyz
Kalaman, Yerbolat
Kryvoruchko, Olena
Desiatko, Alona
Kaminskyi, Serhii
Powiązania:
https://bibliotekanauki.pl/articles/27311974.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
information security
informatization object
distributed computing network
mathematical model
vulnerability coefficient
virtualization
IDS
SIEM
Opis:
A methodology for development for distributed computer network (DCN) information security system (IS) for an informatization object (OBI) was proposed. It was proposed to use mathematical modeling at the first stage of the methodology. In particular, a mathematical model was presented based on the use of the apparatus of probability theory to calculate the vulnerability coefficient. This coefficient allows one to assess the level of information security of the OBI network. Criteria for assessing the acceptable and critical level of risks for information security were proposed as well. At the second stage of the methodology development of the IS DCN system, methods of simulation and virtualization of the components of the IS DCN were used. In the course of experimental studies, a model of a protected DCN has been built. In the experimental model, network devices and DCN IS components were emulated on virtual machines (VMs). The DCN resources were reproduced using the Proxmox VE virtualization system. IPS Suricata was deployed on RCS hosts running PVE. Splunk was used as SIEM. It has been shown that the proposed methodology for the formation of the IS system for DCN and the model of the vulnerability coefficient makes it possible to obtain a quantitative assessment of the levels of vulnerability of DCN OBI.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 3; 475--483
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effect of ship neural domain shape on safe and optimal trajectory
Autorzy:
Lisowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/24201475.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
artificial neural network model
method for optimization
dynamic programming method
ship safety domain
safe ship control
path planning
multi-object decision model
computer simulation
Opis:
This article presents the task of safely guiding a ship, taking into account the movement of many other marine units. An optimally neural modified algorithm for determining a safe trajectory is presented. The possible shapes of the domains assigned to other ships as traffic restrictions for the particular ship were subjected to a detailed analysis. The codes for the computer program Neuro-Constraints for generating these domains are presented. The results of the simulation tests of the algorithm for a navigational situation are presented. The safe trajectories of the ship were compared at different distances, changing the sailing conditions and ship sizes.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2023, 17, 1; 185--191
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48
Autorzy:
Otoom, Mohammad Mahmood
Sattar, Khalid Nazim Abdul
Al Sadig, Mutasim
Powiązania:
https://bibliotekanauki.pl/articles/2201908.pdf
Data publikacji:
2023
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
cyber security
network intrusion
ensemble learning
machine learning
ML
Opis:
Technology is rising on daily basis with the advancement in web and artificial intelligence (AI), and big data developed by machines in various industries. All of these provide a gateway for cybercrimes that makes network security a challenging task. There are too many challenges in the development of NID systems. Computer systems are becoming increasingly vulnerable to attack as a result of the rise in cybercrimes, the availability of vast amounts of data on the internet, and increased network connection. This is because creating a system with no vulnerability is not theoretically possible. In the previous studies, various approaches have been developed for the said issue each with its strengths and weaknesses. However, still there is a need for minimal variance and improved accuracy. To this end, this study proposes an ensemble model for the said issue. This model is based on Bagging with J48 Decision Tree. The proposed models outperform other employed models in terms of improving accuracy. The outcomes are assessed via accuracy, recall, precision, and f-measure. The overall average accuracy achieved by the proposed model is 83.73%.
Źródło:
Advances in Science and Technology. Research Journal; 2023, 17, 2; 322--329
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fault diagnosis model of rolling bearing based on parameter adaptive VMD algorithm and Sparrow Search Algorithm-Based PNN
Autorzy:
Li, Junxing
Liu, Zhiwei
Qiu, Ming
Niu, Kaicen
Powiązania:
https://bibliotekanauki.pl/articles/24200836.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
rolling bearing
failure diagnosis
adaptive variational mode decomposition
sparrow probabilistic neural network
Opis:
Fault diagnosis of rolling bearings is essential to ensure the proper functioning of the entire machinery and equipment. Variational mode decomposition (VMD) and neural networks have gained widespread attention in the field of bearing fault diagnosis due to their powerful feature extraction and feature learning capacity. However, past methods usually utilize experiential knowledge to determine the key parameters in the VMD and neural networks, such as the penalty factor, the smooth factor, and so on, so that generates a poor diagnostic result. To address this problem, an Adaptive Variational Mode Decomposition (AVMD) is proposed to obtain better features to construct the fault feature matrix and Sparrow probabilistic neural network (SPNN) is constructed for rolling bearing fault diagnosis. Firstly, the unknown parameters of VMD are estimated by using the genetic algorithm (GA), then the suitable features such as kurtosis and singular value entropy are extracted by automatically adjusting the parameters of VMD. Furthermore, a probabilistic neural network (PNN) is used for bearing fault diagnosis. Meanwhile, embedding the sparrow search algorithm (SSA) into PNN to obtain the optimal smoothing factor. Finally, the proposed method is tested and evaluated on a public bearing dataset and bearing tests. The results demonstrate that the proposed method can extract suitable features and achieve high diagnostic accuracy.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 2; art. no. 163547
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression
Autorzy:
Wang, Siming
Zhao, Kaikai
Powiązania:
https://bibliotekanauki.pl/articles/24202729.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
distributed generation
distribution network fault location
fault type
Karrenbauer transform
agent prediction model
SVR
support vector regression
Opis:
As the capacity and scale of distribution networks continue to expand, and distributed generation technology is increasingly mature, the traditional fault location is no longer applicable to an active distribution network and "two-way" power flow structure. In this paper, a fault location method based on Karrenbauer transform and support vector machine regression (SVR) is proposed. Firstly, according to the influence of Karrenbauer transformation on phase angle difference before and after section fault in a low-voltage active distribution network, the fault regions and types are inferred preliminarily. Then, in the feature extraction stage, combined with the characteristics of distribution network fault mechanism, the fault feature sample set is established by using the phase angle difference of the Karrenbauer current. Finally, the fault category prediction model based on SVR was established to solve the problem of a single-phase mode transformation modulus and the indistinct identification of two-phase short circuits, then more accurate fault segments and categories were obtained. The proposed fault location method is simulated and verified by building a distribution network system model. The results show that compared with other methods in the field of fault detection, the fault location accuracy of the proposed method can reach 98.56%, which can enhance the robustness of rapid fault location.
Źródło:
Archives of Electrical Engineering; 2023, 72, 2; 461--481
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modelarea cuiburilor derivaționale ale verbelor emotive în limba română
Modelling of derivational networks of emotive verbs in Romanian
Autorzy:
Czarnecki, Szymon
Powiązania:
https://bibliotekanauki.pl/articles/43665729.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet im. Adama Mickiewicza w Poznaniu
Tematy:
word-formation
derivation
word-formation type
semantic-structural type
semantic-structural model
derivational network
Romanian
language modelling
Opis:
The present paper aims to model derivational networks (nests) of emotive verbs in Romanian, on the basis of the semantic-structural types of derivatives. First, we define the most important notions of the nest derivatology, we introduce the notion of derivational nest modelling and of semantic-structural types of derivatives, and describe the principles of the analysis. After that, the derivational nests of selected verbs and their formal-semantic analysis are presented, with semantic and formal aspects in the commentary after each nest. At the end, the matrix of models resulting from the juxtaposition of all nest models and the conclusions from the research are presented.
Źródło:
Studia Romanica Posnaniensia; 2023, 50, 4; 35-53
0137-2475
2084-4158
Pojawia się w:
Studia Romanica Posnaniensia
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Network of trust relationships in the remote work model
Autorzy:
Wawrzynek, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/27313421.pdf
Data publikacji:
2023
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
social network analysis
trust networks
work model
analiza sieci społecznościowych
sieci zaufania
model pracy
Opis:
Purpose: The article aimed to identify differences in the density of the trust network of team members in different work models (on-site, hybrid, and remote) and to identify opportunities for building knowledge and innovation in such work models based on the trust there. The method of experiment and a social networks analysis (SNA) was used to achieve the goal. Design/methodology/approach: The research is based on an experiment as part of a strategic business simulation game. The participants of the investigation are MBA students. The variable in the experiment is the work model. In these three different situations, relationships developed in teams are identified. Based on the identified relationships, visualizations of the trust network were built. Findings: The research confirmed that the hybrid and remote work models minimize the number of trust ties between team members. The network of trust based on the identified relationships is less dense. The decline in confidence leads to the conclusion that a company's innovation and ability to generate new knowledge are now under threat based only on group resources. Research limitations/implications: Research is based on an experiment. The group subjected to the investigation is MBA students. The limited duration of the experiment may limit the formation of networks of trust (based on long-term, deep relationships). See also a summary. Practical implications: The results indicate apparent differences in the density of trust relations between the organization's participants in the three analyzed work models. This points directly to the need to adjust tools supporting the development of innovation and knowledge creation for remote work models, different from those known from traditional (on-site) work models. Originality/value: The study shows that trust relationships, e are more challenging to achieve in remote working conditions than in traditional work models. It gives managers guidelines on what tools (such as SNA) they can use to identify relationships between people in new work models.
Źródło:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2023, 169; 691--705
1641-3466
Pojawia się w:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Novel Hybrid Model Investing in 5G Network Optimization Under Suzuki Fading Channel
Autorzy:
Abed, Muntadher Suhail
Powiązania:
https://bibliotekanauki.pl/articles/27311908.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
5G
Suzuki Fading Channel
polar code
quality of service (QoS)
rate matching
Opis:
Nowadays, the advancement and increased use of fifth-generation (5G) and sixth-generation (6G) systems have created a demand for more efficient and rapid transmission of information over wireless communication media. However, developing wireless communication systems that can meet these modern-day criteria for fast, reliable, and secure information exchange is a challenging task. To address this issue, this paper proposes a novel model for enhancing the 5G system. The proposed model utilizes polar code with rate matching and constitutional interleaving over the Suzuki fading channel. The combination of polar codes with rate matching and interleaving enables the communication system to achieve a lower error rate and better reliability over a Suzuki fading channel. Specifically, the polar code can correct a larger number of errors, while rate matching and interleaving can mitigate the effects of channel variations and reduce the probability of error bursts. These enhancements can lead to more robust and reliable communication in wireless networks.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 3; 553--558
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimising pedestrian flow in a topological network using various pairwise speed-density models
Autorzy:
Khalid, Ruzelan
Nawawi, Mohd Kamal Mohd
Ishak, Nurhanis
Baten, Md Azizul
Powiązania:
https://bibliotekanauki.pl/articles/29127975.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
topological network
speed-density model
optimal arrival rate
network flow model
pedestrian flow
Opis:
A speed-density model can be utilised to efficiently flow pedestrians in a network. However, how each model measures and optimises the performance of the network is rarely reported. Thus, this paper analyses and optimises the flow in a topological network using various speed-density models. Each model was first used to obtain the optimal arrival rates to all individual networks. The optimal value of each network was then set as a flow constraint in a network flow model. The network flow model was solved to find the optimal arrival rates to the source networks. The optimal values were then used to measure their effects on the performance of each available network. The performance results of the model were then compared with that of other speed-density models. The analysis of the results can help decision-makers understand how arrival rates propagate through traffic and determine the level of the network throughputs.
Źródło:
Operations Research and Decisions; 2023, 33, 4; 53--69
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
Autorzy:
He, Ping
Dong, Jie
Wu, Xiaopeng
Yun, Lei
Yang, Hua
Powiązania:
https://bibliotekanauki.pl/articles/27309934.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
BP neural network
photovoltaic power generation
PSO–GWO model
PSO–GWO–BP prediction model
particle swarm optimization
gray wolf optimization
back propagation
standard grey wolf algorithm
Opis:
At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
Źródło:
Archives of Electrical Engineering; 2023, 72, 3; 613--628
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
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
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